University of Ghana http://ugspace.ug.edu.gh UNIVERSITY OF GHANA FOREIGN DIRECT INVESTMENT AND INDUSTRIALISATION IN SUB-SAHARAN AFRICA: THE ROLE OF FINANCIAL INSTITUTIONS DEVELOPMENT BY PETER PARKU (10551274) THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF A MASTER OF PHILOSOPHY (MPHIL) DEGREE IN FINANCE. JULY, 2018. University of Ghana http://ugspace.ug.edu.gh DECLARATION I, PETER PARKU, hereby declare that this thesis is an original research undertaken by me under the guidance of my supervisors, and with the exception of references to other people‟s work which have been duly cited, this thesis has neither in part nor in whole been submitted for another degree elsewhere. ……………………………. ……………………………. PETER PARKU DATE (10551274) i University of Ghana http://ugspace.ug.edu.gh CERTIFICATION I hereby certify that this thesis was supervised in accordance with the procedures laid down by the University of Ghana. ……………………………. ……………………………. DR. ELIKPLIMI KOMLA AGBLOYOR DATE SUPERVISOR …………………………………… …………………………… DR. AGYAPOMAA GYEKE-DAKO DATE SUPERVISOR ii University of Ghana http://ugspace.ug.edu.gh DEDICATION This work is dedicated to my wife Mrs. Gifty Parku Agbley for her encouragement throughout the period of my studies and to my sons Cecil Edem Parku and Cephas Selasie Parku. iii University of Ghana http://ugspace.ug.edu.gh ACKNOWLEDGEMENTS I thank God Almighty for His grace throughout the period of this study. I would like to express my sincere appreciation to my supervisors Dr. Elikplimi Komla Agbloyor and Dr. Agyapomaa Gyeke-Dako for their guidance, comments and constructive criticisms which shaped and enriched the quality of this study. I want to use this study as a platform to acknowledge a few people who supported my education and personal development over the years and offered their support for the research. I want to do this because I think it is the best platform to appreciate them for all that they did and have done for me, which propels me into discovering and pursuing my purpose, passion and life dreams. First of all, I thank my parents, Mr. and Mrs. Parku so much for doing all that they could to support my education. I am so much indebted to Mr. Richard Kafui Parku, a brother and Mrs. Cecilia Fiaka, a sister for their financial supports during my Senior Secondary School and Teacher Training College education respectively. I thank my spiritual fathers Bishop K. Baiden and Bishop E. L. Nterful for their council, encouragements and spiritual support. Finally, in spite of the careful and painstaking research for over a year to bring out this work, there is no doubt that there may be some oversights and errors. I am singularly liable for any omission and errors that might be found in the research. iv University of Ghana http://ugspace.ug.edu.gh ABSTRACT The objective of the study was to analyse the role of financial institutions development in the link between FDI and industrialisation in SSA. Employing the static model (Fixed Effect Model) and the dynamic model (Generalised Method of Moments), the study utilises a panel model of 45 countries in SSA spanning the period 1990 to 2009. The findings of the study reveal that previous year output of the manufacturing industry is very essential to the industrialisation process in SSA. Additionally, FDI has a significant but negative link with the output of the manufacturing sector. Similarly, financial institutional development has a negative effect on industrialisation. However, the results indicate that the interaction between FDI and the financial institution variable has a positive and significant effect on industrialisation. The results also show that Gross Domestic Product per capita (GDPC) has a significant and positive effect on industrialisation. Based on the findings of the study, SSA countries should implement measures to streamline FDI inflows into the manufacturing industry as well as formulate and implement important industrial policies. To promote effective industrialisation, SSA countries should improve the resilience of their financial system in order to positively intermediate FDI inflows into the manufacturing industry. Key words: Industrialisation, Foreign Direct Investment, Financial institution, Gross Domestic Product, Export, Inflation, Human capital and Population. v University of Ghana http://ugspace.ug.edu.gh TABLE OF CONTENTS DECLARATION ................................................................................................................................... i CERTIFICATION ................................................................................................................................ii DEDICATION .................................................................................................................................... iii ACKNOWLEDGEMENTS ................................................................................................................ iv ABSTRACT .......................................................................................................................................... v TABLE OF CONTENTS .................................................................................................................... vi LIST OF FIGURES .............................................................................................................................. x LIST OF TABLES ............................................................................................................................... xi LIST OF ACRONYMS ..................................................................................................................... xii CHAPTER ONE ................................................................................................................................... 1 INTRODUCTION ................................................................................................................................ 1 1.1 Background to the Study ....................................................................................................... 1 1.2 Problem Statement ................................................................................................................ 3 1.3 Research Questions ............................................................................................................... 5 1.4 Research Objectives .............................................................................................................. 5 1.5 Research Hypothesis ............................................................................................................. 5 1.6 Significance of the Study ...................................................................................................... 5 1.7 Scope of the Study ................................................................................................................. 6 1.8 Organization of the Study ..................................................................................................... 7 vi University of Ghana http://ugspace.ug.edu.gh CHAPTER TWO .................................................................................................................................. 8 OVERVIEW OF FDI AND INDUSTRIALISAION IN SUB-SAHARAN AFRICA..................... 8 2.1 Introduction ............................................................................................................................ 8 2.2 Trends of FDI inflows in Sub-Saharan Africa ..................................................................... 8 2.3 Chapter Summary ................................................................................................................ 10 CHAPTER THREE ............................................................................................................................ 11 LITERATURE REVIEW ................................................................................................................... 11 3.1 Introduction .......................................................................................................................... 11 3.2 Theoretical Review.............................................................................................................. 11 3.2.1 Industrialization ........................................................................................................... 11 3.2.2 Foreign Direct Investment (FDI) ................................................................................ 12 3.2.3 FDI and its Impact on Industrialisation ...................................................................... 14 3.2.4 Determinants of Industrialisation................................................................................ 15 3.3 Empirical Studies on Industrialisation and FDI ................................................................ 19 3.4 Chapter Summary ................................................................................................................ 29 CHAPTER FOUR .............................................................................................................................. 30 METHODOLOGY ............................................................................................................................. 30 4.1 Introduction .......................................................................................................................... 30 4.2 Model Specification ............................................................................................................ 30 4.2.1 Static Model ................................................................................................................. 30 vii University of Ghana http://ugspace.ug.edu.gh 4.2.2 Dynamic Model ............................................................................................................ 31 4.3 Description of Variables Employed in the Empirical Analysis ........................................ 32 4.4 Estimation Technique .......................................................................................................... 36 4.5 Diagnostics Tests ................................................................................................................. 43 4.5.1 Multicollinearity .......................................................................................................... 44 4.5.2 Heteroscedasticity and Autocorrelation ..................................................................... 44 4.5.3 Endogeneity .................................................................................................................. 45 4.6 Data Sources and Sample Size ........................................................................................... 46 4.7 Chapter Summary ................................................................................................................ 46 CHAPTER FIVE ................................................................................................................................ 47 RESULTS AND DISCUSSION OF FINDINGS ............................................................................. 47 5.1 Introduction .......................................................................................................................... 47 5.2 Descriptive Statistics ........................................................................................................... 47 5.3 Diagnostic Tests Results ..................................................................................................... 48 5.3.1 Multicollinearity Results ............................................................................................. 49 5.3.2 Auto Correlation and Heteroscedasticity Results ...................................................... 49 5.4 Empirical Results................................................................................................................. 50 5.5 Chapter Summary ................................................................................................................ 57 CHAPTER SIX ................................................................................................................................... 58 SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS ........................... 58 viii University of Ghana http://ugspace.ug.edu.gh 6.1 Introduction .......................................................................................................................... 58 6.2 Summary of Findings .......................................................................................................... 58 6.3 Conclusion ........................................................................................................................... 59 6.4 Policy Recommendations .................................................................................................... 61 6.5 Suggestions for Future Research ........................................................................................ 61 6.6 Limitations of the study ...................................................................................................... 62 REFERENCES ................................................................................................................................... 63 APPENDICES .................................................................................................................................... 71 Appendix A: Hausman Test -Independent Effect of FDI and Financial Institution ................... 71 Appendix B: Hausman Test -Interactive Effect between FDI and Financial Institution ........... 72 Appendix C: Correlation Matrix .................................................................................................... 72 Appendix D: Variance Inflation Factor ......................................................................................... 73 Appendix E: Autocorrelation Test ................................................................................................. 73 Appendix F: Heteroscedasticity Test ............................................................................................. 73 Appendix G: List of countries by sub-region ................................................................................ 74 Appendix H: Data ........................................................................................................................... 75 ix University of Ghana http://ugspace.ug.edu.gh LIST OF FIGURES Figure 2.1: Annual Average of FDI Inflows across Africa sub-regions (1980-2012) ..................... 9 x University of Ghana http://ugspace.ug.edu.gh LIST OF TABLES Table 3.1: Summary of Empirical Studies on Industrialisation and FDI ........................................ 28 Table 4.1: Summary Description of the Variables Employed in Regression ............................... 536 Table 5.1: Summary Descriptive ....................................................................................................... 53 Table 5.2: The Independent Effect of FDI and Financial Institutions on Industrialisation ........... 53 Table 5.3: The Interactive Effect between FDI and Financial Institutions on Industrialisation .... 56 xi University of Ghana http://ugspace.ug.edu.gh LIST OF ACRONYMS ADI - A frican Development Indicators CPI - C onsumer Price Index DWH - D urbin–Wu–Hausman FEM - Fixed Effect Model FDI - F oreign Direct Investment GMM - G eneralized Method of Moments GDP - G ross Domestic Product IV - I nstrumental Variable ISIC - I nternational Standard Industrial Classification MNC - Multinational Corporations OLS - O rdinary Least Squares REM - R andom Effect Model SSA - S ub-Saharan Africa 3.S.LS - T hree Stage Least Squares TFP - T otal Factor Productivity 2SLS - T wo-Stage Least Square Estimation UK - U nited Kingdom U.S. - U nited States VIF - Variance Inflation Factor VECM - V ector Error Correction Model WDI - W orld Development Indicators xii University of Ghana http://ugspace.ug.edu.gh CHAPTER ONE INTRODUCTION 1.1 Background to the Study Industrialization is an ongoing process in Africa and other developing countries. In 2013, the value-added share of the manufacturing industry in Sub-Saharan Africa (SSA) recorded an average rate of 11 percent which is similar to that of the 1990s. During the same year, the share of worldwide Foreign Direct Investment (FDI) flows into SSA decreased steadily (Chen, Geiger & Fu, 2015). Additionally, regional statistics indicate that Africa on average recorded a 5.68 percent decline in the value-added share of the manufacturing industry during the 1980 to 2009 period. The decline in industrial development in Africa has been attributed to host country‟s inability to attract FDI (Gui-Diby & Renard, 2015). Foreign Direct Investment (FDI) is an important source of capital flows in the industrial policies of many developing countries. Multinational Corporations (MNC) perform an important role in influencing the growth and development of transition economies. FDI, through the establishment and direct investment by multinational enterprises, serve as a means to create jobs, promote trade and economic integration, transfer of technological knowledge and is a major source of capital for industries in host countries (Costa & Filippov, 2008). The inflow of FDI in the manufacturing sector provides more job opportunities than of FDI in any other sector. Industrialization has resulted in the creation of a considerable number of jobs in the manufacturing sector in SSA countries such as Ethiopia, Tanzania, and Uganda. The 2013 to 2014 data on FDI shows that Tanzania‟s manufacturing industry recorded 43 percent of total jobs created, which is three times more compared to the jobs created in agriculture. Similarly, in 1 University of Ghana http://ugspace.ug.edu.gh 2012, the manufacturing sector in Uganda attained the biggest rate of job creation in the country, recording 30 percent of the total inflow of FDI in other sectors (Costa & Filippov, 2008). The majority of the job creation initiatives in the manufacturing sector may be attributed to non- traditional investors. However, there is inadequate formal training in the manufacturing sector. Dabor (2015), posits that FDI provides a means for transition countries to improve their domestic investment, gain access to foreign markets, stimulate a business culture and trigger an improvement in the standard of living. Existing literature states that FDI inflows into the manufacturing sector enhance changes in goods and job opportunities from foreign investors to host economies resulting in industrialisation. The inflows of FDI do not always yield positive effect for host countries. The financial sector and the government both matter in channelling the flow of FDI to the manufacturing sector resulting in industrialisation. Financial markets perform an essential role in the industrialisation process because they assist in allocating resources (FDI flows) towards productive investments (Adjasi, Abor, Osei, & Nyavor- Foli, 2012). Extant literature posits that any positive effect of FDI inflows on industrialisation and growth may largely depend on the financial institutional development in a receiving country. Bailliu (2000) posits that countries with improved banking structures are better able to absorb the benefit from capital flows. Other studies by Alfaro, Chanda, Kalemli-Ozcan and Sayek (2004) suggest that a well-developed financial system within a host country could contribute to the link between FDI and economic development. According to Da Rin and Hellmann (2002), well- developed banks serve as a catalyst to the industrial process through resource allocation to the manufacturing sector. Generally, the policy emphasis has been on how a country is able to attract FDI flows. The positive externalities associated with FDI is determined by the institutional and economic 2 University of Ghana http://ugspace.ug.edu.gh capabilities of a host economy. Existing literature suggests that FDI flows enhance industrialisation under specific, but not explicit factors. These factors involve stability of the macroeconomic environment, trade liberalization, well-developed financial institutions, good governance, development of human capital and infrastructure. Thus, the government and the financial system perform an important role in reducing the magnitude of any potential negative effects associated with FDI and industrialization. 1.2 Problem Statement Industrialisation in Africa is comparatively lower than the rest of the continents. Scholars and researchers have attributed the slow pace of industrialisation in Africa, specifically Sub-Saharan Africa, to the challenges of attracting foreign direct investment. The average share of manufacturing to GDP in Sub-Saharan African countries from FDI in 2013 was 11 percent (World Bank, 2014 report). The 11 percent was almost unchanged from the 1990s. World Bank (2014) indicated that the share of worldwide FDI flow, at the same time, to SSA has been rather low. Africa adopted an action plan in 2008 for the acceleration of industrial development in Africa. Paramount to the plan is the need to enhance industrialisation in Africa through Foreign Direct Investment. World Bank‟s (2014) report on industrialisation in Africa observed that most of the countries in SSA have not taken off significantly in industrialisation. Rodrik (2015) found that the growth of industrialisation GDP per capita, for the past 20years, was 1.26 percent on average per year and relatively lower than the service sector which grew by 1.33 percent on average per year. There is the need to enhance the flow of FDIs into SSA countries since it serves as a catalyst for growth by boosting the host countries‟ economic growth by providing much needed 3 University of Ghana http://ugspace.ug.edu.gh capital, creating new jobs, generating productivity spillovers, and transferring technology, skills, and management know-how (Prasad et al. 2003). To be able to enjoy the benefits associated with FDI, it is very crucial that SSA countries position themselves very well to be able to attract FDI. There are basically two factors that influence the flow of FDI into a country, which SSA countries should consider. Calvo et al. (1993) asserted that factors that are related to the intrinsic motivation of the investor influences FDI flows to a country. On the supply side, Morisset (2000) and Collins (2002) both found that motivating factors emanate from the characteristics of the host country which may include macroeconomic policy and performance, trade openness, tax levels and incentives, the quality of legal and other institutions and many other factors. Financial institution plays a very critical role in influencing the flow of FDIs into an economy and subsequently enhances industrialisation. Alfaro, Chanda, Kalemli-Ozcan and Sayek (2004) provide evidence that to gain significant flow of FDI, countries need to have well developed financial markets. Alfaro, Chanda, Kalemli-Ozcan and Sayek (2010) examined the role of local financial markets in enabling FDI to flow. The study found that economies with well-functioning financial markets benefit from the backward linkages between the foreign and domestic firms with positive spillovers to the rest of the economy. Hermes and Lensink (2010) used data from 67 countries in Latin America and Asia and observed that countries with well-developed financial system gain positive contribution to their economies from FDI. In spite of the extant literature on FDI and financial institutions development, mostly in developed countries, to the best of my knowledge, no work has been done on how financial institutions development influences FDI and industrialisation in SSA and hence the study is unique in this respect. It is against this backdrop that this study intends to critically evaluate how financial institutions development, an aspect of the supply side of FDI flows into a country 4 University of Ghana http://ugspace.ug.edu.gh impacts FDI and industrialisation in SSA countries. This is very essential taking into account the important role FDI plays in industrialisation and economic development and hence an investigation into the extent at which financial institutions development influence FDI and industrialisation in SSA, which have recorded low rate of FDI flow on industrialisation growth. 1.3 Research Questions  What is the effect of FDI and financial institution on industrialization in SSA?  Does the financial institution play a role in determining FDI‟s impact on industrialization in SSA? 1.4 Research Objectives The main objective of the study is to analyse the role financial institutions development play in the relationship between FDI and industrialization in SSA. The specific objectives are as follows:  To examine the effect of FDI inflows and financial institutions on industrialization in SSA.  To analyse the role of financial institutions development in determining FDI impact on industrialization in SSA. 1.5 Research Hypotheses : FDI inflows do not have any effect on industrialization in SSA. : Financial institutions do not have any effect on industrialization in SSA. : Financial institutions are not necessary for FDI impact on industrialization in SSA. 1.6 Significance of the Study While studies by Adam (2009) and Feeny, Iamsiraroj and McGillivray (2014) have focussed on the relationship between FDI and economic growth, very few studies have examined the link 5 University of Ghana http://ugspace.ug.edu.gh between industrialization and FDI in SSA. This study contributes to the existing studies by investigating the effect of FDI on industrialization. The rationale behind this objective is to assess whether policies aimed at attracting FDI inflows have been incorporated in industrial policies since the establishment and direct investment by multinational enterprises serve as a means to create jobs, promote trade and transfer of technological know-how to the manufacturing sector in host countries. In addition, it examines how FDI inflows influence industrialization by taking into account the role of financial institutions development. The study‟s approach is innovative and different from Gui-Diby and Renard (2015) because the study interacts FDI inflows with a proxy for financial institutional development. The reason underlying this is to investigate whether the development of financial institutions in a host country is a necessary condition for FDI flows to have any effect on industrialization. The study is similar to the work of Agbloyor, Abor, Adjasi and Yawson (2014) in which they focussed on the contribution of financial market development in promoting capital flows on economic growth. The main point of departure is that this study focuses on the interactive effect between financial institutional development and FDI inflow on industrialization. Furthermore, the study conducts hypothesis testing based on SSA data, which has been neglected in the area of research. 1.7 Scope of the Study The study uses a panel model with data culled from the African Development Index (ADI) and World Development Indicators (WDI) for 45 selected countries from SSA spanning the period 1990 to 2009. The study relies on annual data that are available and accessible for each country during the study period. The starting year of the study corresponds to the year in which most 6 University of Ghana http://ugspace.ug.edu.gh countries in SSA adopted the liberalization of the financial sector as proposed by the International Monetary Fund (IMF) and the World Bank in order to remove the vestiges of stiff competition, highly regulated interest rates and limitations of the financial markets. 1.8 Organization of the Study The outline of the study is structured as follows: Chapter One introduces the background to the study, problem statement, research questions, and objectives of the study. It further explains the motivation and the relevance of the study as well as it highlights the scope of the study. Chapter Two provides an overview of FDI and Industrialisation in SSA. Chapter Three presents a review of the theoretical and empirical literature on FDI and industrialization. Chapter Four discusses the methodology which includes the empirical model, the estimation techniques and sources of data. Chapter Five provides a presentation and discussion of results. Chapter Six concludes with a summary and the provision of recommendations as well as limitations of the study. 7 University of Ghana http://ugspace.ug.edu.gh CHAPTER TWO OVERVIEW OF FDI AND INDUSTRIALISAION IN SUB-SAHARAN AFRICA 2.1 Introduction This chapter provides an overview of FDI and industrialisation in SSA. It further presents the trend of FDI inflows in Sub-Saharan Africa (SSA). 2.2 Trends of FDI inflows in Sub-Saharan Africa The inflow of FDI into Africa is relatively increasing and varying across sub-regions compared to the past. FDI inflows into SSA have increased to about $45 billion in 2000 and a further increment to $474 billion in 2013 indicating a six-fold expansion. While FDI inflows into Africa seem to have relatively high returns on investment, profitability in the manufacturing sector is much higher compared to other sectors. The inflow of FDI in the manufacturing sector provides more job opportunities than FDI in any other sector. Industrialization has resulted in the creation of jobs in sectors in SSA countries such as Ethiopia, Tanzania, and Uganda. The 2013 to 2014 data on FDI shows that Tanzania‟s manufacturing industry recorded 43 percent of total jobs created which is three times more compared to the jobs created in agriculture. Similarly, in 2012, the manufacturing sector in Uganda attained the biggest job creation in the country, recording 30 percent of the total inflow of FDI in other sectors. Most of the jobs created in the manufacturing sector may be attributed to non-traditional investors. However, there is inadequate formal training in the manufacturing sector. Although developed economies serve as the primary source of direct investment by multinationals in the Sub-Saharan region, there has been an increasing flow of foreign direct investment by emerging economies including in other regions in Africa. 8 University of Ghana http://ugspace.ug.edu.gh Over the last decades, FDI flow into the sub-Saharan region has been decreasing if not increasing at a decreasing rate. During the early 1980‟s SSA recorded 6 percent of the FDI flow across the world, with a further decline of 0.5 percent in 2000 and 2.2 percent in 2005. This decreased may stem from the fact that developed countries with larger markets attract the higher flow of FDI. The UNCTAD, 2005 report indicates that SSA recorded 34 percent of the inward FDI, with 28 percent in developing economies and 21 percent in developed countries. Africa West Africa Southern Africa East Africa Central Africa 90 80 70 60 50 40 30 20 10 0 1 9 8 0 - 8 4 1 9 8 5 - 8 9 1 9 9 0 - 9 4 1 9 9 5 - 9 9 2 0 0 0 - 0 4 2 0 0 5 - 0 9 2 0 1 0 - 1 2 -10 Figure 2.1: Annual Average of FDI Inflows across Africa sub-regions (1980-2012) Source: Kudaisi (2014) As shown in Table 2.1, Southern Africa has been performing quite high in attracting FDI since 1980. However, South Africa recorded a negative inflow of FDI during the period 1985-1989. This can be attributed to the period of apartheid when a debt crises and economic sanctions were prevalent in the country. It experienced higher inflow after 1990 after the lifting of the economic sanctions. West Africa has witnessed a substantial inflow of FDI of about 61percent and 56 percent, during the periods 2005-2009 and 2010-2012 respectively. The least annual average of 9 University of Ghana http://ugspace.ug.edu.gh FDI inflows were recorded by East Africa during the period of 1980 to 1984, and the period 2000-2004. This may be attributed to political instability and trade barriers in some of the countries in the Eastern region of Africa. The period 2000 to 2012 has accounted for a significant increase in the flow of FDI to Sub-Saharan Africa due to some level of political stability, economic integration and financial development in most countries in the Sub-Saharan region. 2.3 Chapter Summary The inflow of FDI in the manufacturing sector provides more job opportunities than FDI in any other sector. Industrialization has resulted in the creation of jobs in sectors in SSA countries such as Ethiopia, Tanzania, and Uganda. Although developed economies serve as the primary source of direct investment by multinationals in the Sub-Saharan region, there has been an increasing flow of foreign direct investment by emerging economies including in other regions in Africa 10 University of Ghana http://ugspace.ug.edu.gh CHAPTER THREE LITERATURE REVIEW 3.1 Introduction This chapter presents an overview of industrialization in SSA, discusses the theoretical literature on industrialization, on FDI and the relationship between FDI and industrialization as well as the determinants of industrialization. In addition, it summarizes the extant literature on FDI and industrialization. 3.2 Theoretical Review 3.2.1 Industrialization Industrialization, as defined by Chandra (1992), is the increase in the value-added share of the manufacturing industry in terms of the Gross Domestic Product (GDP). In other words, industrialization refers to the growth recorded in the manufacturing industry relative to other sectors (agricultural and service). Industrialization related to the manufacturing sector implies a decline in the additional value share of agriculture and a rise in the manufacturing sector. The rise of industrialization based on the growth of the industry is also defined as the development of an advanced manufacturing industry, which includes the establishment of enterprises, specialization, and advanced technology. Industrialization may be viewed as a social and economic process which involves „a rapid transformation in the significance of manufacturing activity in relation to all other forms of production and work undertaken with national (or local) economies‟ (O‟Brien, 2001). The positive relationship between the increasing output of the manufacturing industry and development of the economy growth is attributed to the characteristics of the manufacturing sector being more productive as well as more capital intensive relative to other sectors. Manufacturing is also considered as the locus of technological 11 University of Ghana http://ugspace.ug.edu.gh progress because capital goods embody state-of-the-art technologies and learning accumulates with production (Cornwall, 1977). The manufacturing industry is very important to economic growth and development. 3.2.2 Foreign Direct Investment (FDI) FDI may be defined as secure investment to develop a long serving management interest of an operating company in a host country with the aim of the investor having an operative role in the management of the company. It is the sum of reinvestment of earnings, equity capital and other short and long-term capital recorded in the balance of payments (Zu Selhausen, 2009). FDI varies in its mode of entry and by its size; the trade direction of its mother company; the role of its subsidiaries in the global network; the objective for investment and the impact on host countries. There are three main types of FDI according to the mode of entry and its impact on host countries. 3.2.2.1 Resource Seeking FDI Resource-seeking FDI is a type of FDI which is motivated by an investor interest in accessing and exploiting natural resources. It is often considered as having a limited overall effect on economies. Studies in Africa indicate that resource seeking FDI mostly leads to less job creation as well as the lack of any positive spillover effect in the short run relative to the other types of FDI. Zu Selhausen, (2009) argues that resource-seeking FDI dominance does not enable SSA to benefit from its return on capital potential. Although SSA absorbs a greater percentage of resource-seeking FDI compared to Latin America and Asian countries, the natural resources in SSA are usually traded away rather than being processed in the region itself. Therefore, resource- seeking FDI does not translate into sustained economic growth nor institutional change but consequently crowds out the manufacturing sector. 12 University of Ghana http://ugspace.ug.edu.gh 3.2.2.2 Market Seeking FDI Marketing seeking FDI is generated by an investor interest in serving domestic or regional markets. It mainly exists in the manufacturing and service sector and it has quite an amount of positive spillover to the host country. Market-seeking FDI in the service and manufacturing sector can be beneficial to the host economy through job creation, production of quality goods and services and developments of domestic products and markets. Farole and Winkler, (2014) assert that market-seeking FDI is more likely to be integrated into the host country, for better use of domestic markets and to provide assistance to suppliers relative to the other types of FDI. Marketing-seeking FDI is attracted to areas with better trade regulations, indicating the investors‟ interest in protected markets and import-substituting investment. 3.2.2.3 Efficiency Seeking FDI Efficiency-seeking FDI is the type of FDI that comes into a country looking to integrate into the global economy. It exhibits a stronger growth effect relative to resource and market-seeking FDI. Efficiency-seeking FDI mainly exists in the manufacturing sector. Nunnenkamp and Spatz (2004) are of the opinion that the effect of FDI on industries that absorb efficiency-seeking FDI is due to the fact that efficiency seeking FDI transfers technology and knowledge to host countries; enables domestic suppliers and firms to reap the positive contribution through adjustment and imitation and generates earnings on foreign exchange for a host economy. 13 University of Ghana http://ugspace.ug.edu.gh 3.2.3 FDI and its Impact on Industrialisation Existing literature posits that FDI inflows in the manufacturing sector can induce changes in goods, the creation of jobs and transfer of technological knowledge from foreign investors to host economies resulting in industrialization. In addition, in the industrialisation process, the government performs a crucial role in addressing economic challenges such as market failure and reduction of negative spillover effects arising from FDI flow. Also, government can enhance human capital through the training of labour. The effect of FDI flows on the manufacturing sector, can be categorized into the following: 3.2.3.1 Competition Effect Foreign subsidiaries established by Multinational Corporation (MNC) in general have a competitive advantage that enables them to compete with domestic firms despite the domestic firms‟ advantage in terms of superior knowledge of domestic market conditions, language, and culture. The competitive advantages stem from technological knowledge, international expertise, better management skills and advanced technologies. The entry of MNC may stimulate domestic firms to preserve their profits resulting in higher productivity. An increase in productivity arises as local companies exploit resources and technology efficiently in their usage of more superior and efficient technology or through the imitation of technology employed by MNC. Additionally, the home-field advantage of domestic firms may enhance local providers of intermediate goods to compete with foreign providers in terms of inputs and raw materials. 14 University of Ghana http://ugspace.ug.edu.gh 3.2.3.2 Training of Domestic Labour Multinational Corporations (MNCs) mainly hire local workforce by offering them training to aid in their performance of work. The training of the domestic labour force varies from seminars to on-the-job training and advanced education within the host country or the parent country of MNCs. In addition, local labour working in an MNC subsidiary may obtain requisite and essential experience and expertise. These great skills and knowledge range from marketing, managerial skills and technical know-how to quality controls. The positive spillover effects to the host country occur as the employees transfer their knowledge and skills to other companies or establish their own firms. 3.2.3.3 Technological Transfer The technological transfer involves the transfer of technological knowledge to local industries. It can increase the productivity of domestic companies as well as profit of firms. The transfer of technology arising from FDI takes place when domestic enterprises or companies imitate technology used by MNC. A multinational corporation‟s subsidiaries generate competition and valued added to the manufacturing industry within the host economy which results in industrialization. 3.2.4 Determinants of Industrialisation In theory, there are several factors that determine or affect industrialization. These factors range of institutional, financial and socioeconomic factors. The study considers some of the essential factors that determine industrialization based on the extant literature. 15 University of Ghana http://ugspace.ug.edu.gh 3.2.4.1 Trade Openness Trade liberalization involves access to affordable prices of imported goods, access to technology and capital, which enhance growth in the industry of developing countries. FDI inflows in the manufacturing industry may transfer managerial skills, technology, capital and superior marketing techniques which could contribute to growth and exports of goods which will, in turn, result in industrialization for the host economy. On the other hand, in an economy where trade barriers exist, companies may experience less knowledge of technical change across the globe as well as exhibit little motivation to employ the practice of innovation. The use of less developed technology and an increase in the cost of production lead to less FDI inflow and integration to the world economy which may hinder industrialization in the host country. Industrialization has often been associated with the openness of an economy with regards to the manufacturing industry that relies mainly on imports. Barua and Chakraborty (2006) examined the manufacturing industry and the role of exports in India. They found evidence that trade openness fosters competition, which enhances efficiency and transfer of knowledge. Gui-Diby and Renard (2015) in their study analysed the link between industrialization and FDI. Using data from African economies, the results reveal that trade openness is a significant channel for industrialization in Africa. 3.2.4.2 Macroeconomic Stability Another factor that may determine industrialization is the macroeconomic stability of the host country. Basically, the stability of the economy in relation to macroeconomic issues promotes industrial growth in that companies operate more efficiently. Macroeconomic stability may stem from the inflation rate or the exchange rate. The general increase in prices (inflation) may result in high cost of production which may lead to a decline in the industrialization process. Similarly, 16 University of Ghana http://ugspace.ug.edu.gh a depreciation of the local currency as well as volatility in the exchange rate may lead to high cost of exporting goods which will lead to difficulty in the production of goods and services by domestic firms. Umer and Alma (2013), using a time series data from the 1960-2011 period in Pakistan, found evidence of the real exchange rate having a positive and significant impact on the industrial sector. Guadagno (2016) analysed the determinants of industrialization in 74 countries during 1960- 2005. Contrary to theories of industrialization associated with a low inflation rate, the study reported a significant and a positive effect of inflation on industrialization. 3.2.4.3 Human Capital Human capital development through training of employees and personnel with expertise is very useful to the industrialization process. Training of personnel in the form of formal education and seminars could result in a competitive manufacturing industry, which will enhance domestic companies to attract direct investments from outside. Enhancing human capital to attract the benefits emanating from FDI is a channel to improve the industrialization process. Hence training of the local workforce through vocational and formal training are essential to industrialization. Borensztein, De Gregorio and Lee (1998), assert that FDI is most productive in the local industry only when there is an adequate stock of human capital. 3.2.4.4 Economic Development Economic development as measured by the gross domestic product per capita is defined as the total value of economic activity within an economy over a period of time. Kaya (2010) in his study on the effect of globalization from 64 manufacturing industry in emerging economies during the 1980-2003 period found evidence that Gross Domestic Product (GDP) per capita as a proxy for economic development is an essential determinant of industrialization. Anaman and 17 University of Ghana http://ugspace.ug.edu.gh Amponsah (2009), examined the factors that determine the manufacturing output in Ghana using an annual time series data from 1974-2006. The findings reveal that the level of the output of the manufacturing sector, in the long run, is influenced by the real GDP per capita. 3.2.4.5 Governance Government plays a key role in the industrialization process. The role of government spans from ensuring training of a local workforce to spurring up the positive spillover effect associated with FDI. Political stability, rule of law, accountability and well-structured institutions generate a conducive environment for domestic enterprises. In addition, government can help reduce the magnitude of some of the negative effect of FDI to domestic companies, since the flow of FDI is not always beneficial to host countries. On the contrary, unnecessary government interventions, as well as government instability and mismanagement in the form of corruption, may put constraints on the functioning of the markets as well as price distortions which may hinder industrialization. Beji and Belhadj (2014), examined the main determining factors of industrialization in Africa. The findings of the study reveal that governance is an essential channel for industrialization in Africa. 3.2.4.6 Financial Institutional Development Financial institutions perform an essential role in the industrialization process because they assist in allocating resources (FDI flows) towards productive investments. Basically, a strong financial market through financial institutions is able to mobilize savings through their intermediation role for trade, productive services, and economic growth. Thus, financial institutions are beneficial to the growth and financial market development of an economy. Financial institutions such as banks aid in the allocation of resources to domestic enterprises in the manufacturing industry by channelling funds from lenders and borrowers, based on its 18 University of Ghana http://ugspace.ug.edu.gh competitive advantage in sourcing information and pooling risk. Extant literature posits that the positive effect of FDI inflows on industrialization and growth may largely depend on the financial market development in a receiving country. Bailliu (2000) is of the opinion that countries with improved banking structures are able to absorb the benefit from capital flows. Other studies by Da Rin and Hellmann (2002), assert that well-developed banks serve as a catalyst to the industrial process through resources allocation to the manufacturing sector. Kaur, Yadav and Gautam (2013), analysed the effect of the financial system on FDI from 29 countries covering the period 1991-2010. The findings of the study reveal a positive effect of FDI on the banking sector and the stock market capitalization. 3.3 Empirical Studies on Industrialisation and FDI Borensztein, et al. (1998), using a cross-country data on 69 industrial economies spanning the 1970-1989 period, investigated the effect of the foreign direct investment on economic growth employing a regression framework. They found evidence that direct investments by foreigners are an essential tool for transfer of technology and an important contributor to the growth of an economy compared to the investments made by domestic companies. Further, the results suggest that FDI is more productive only when there is an adequate stock of human capital and a developed absorptive capacity of modern technology in a host economy. Markusen and Venables (1999) in their study „Foreign Direct Investments as a Catalyst to Industrial Development‟ employed an analytical framework to examine the effect of a multinational company‟s entry in the manufacturing industry of a host country. The study sought to assess the competition effect for which MNCs provide competition for local producers of final goods and the linkage effect of producers of intermediate goods as well as the determining factor of these industrial linkages. The evidence from the study shows that FDI serves as a catalyst that 19 University of Ghana http://ugspace.ug.edu.gh promotes the development of the domestic manufacturing industry as well as strengthening the domestic industry to reduce the absolute role of MNCs in the manufacturing industry. The study further recommends host economies to identify the traits of industries and project in which FDI is likely to contribute positively to the development of the industry. Aitken and Harrison (1999), analysed whether domestic companies gained from foreign direct investment on the Venezuelan manufacturing industry during the 1976-1989 period. Using a weighted least squares estimation on a panel data set of 4000 Venezuelan plants, the findings of the study reveal that, foreign investments have an adverse effect on the productivity of domestic- owned plants. On the contrary, direct investments by foreigners contribute positively to the productivity of plants with less than 50 employees, suggesting that there are positive spillover effects on these plants from foreign owners. Overall, the net effect of direct investment by foreigners in the economy is relatively small as the positive effect of recipient firms outweighs the negative effect of domestic-owned firms. Liu, Siler, Wang and Wei (2000), utilizing a data from 48 United Kingdom (UK) firms covering the period 1991- 1995, investigated the productivity spillovers of FDI within the UK manufacturing industry. Using a Caves-type single–equation technique and simultaneous equations a test is conducted for transfer of technology emanating from direct investments of foreign firms in manufacturing industries. The results show that FDI, capital, training and technological capacities are the determinants of productivity in UK firms. Thus, the entry of MNCs leads to beneficial productivity spillovers in UK industries. Further, the results suggest that the spillover effects resulting from FDI are enormous given the capacity of domestic firms to absorb the technology transfer of MNCs. The study recommends the government to promote the 20 University of Ghana http://ugspace.ug.edu.gh development of domestic enterprise capabilities through the upgrading of skills and knowledge in order to maximize the technological spillovers from FDI. Li, Liu and Parker (2001), using three-stage least squares in a simultaneous model, investigated the influence of FDI on the competition between domestic and foreign industries in China spanning 191 industries in 1995. The results show that the magnitude at which spillovers occur differs from the various forms of domestic enterprises ownership as well as the various types of FDI. The study found evidence that market-seeking FDI leads to positive externalities, usually through competition from domestic enterprises. In addition, domestic owned firms benefit from the demonstration and contagion effect due to the entry of foreign enterprises. The study recommends the need for policy makers to promote independence and motivation of public- owned firms in order to maximize the positive spillovers from the foreign firms as well as target the various types of foreign direct investment through planning for each industry. Girma, Greenaway, and Wakelin (2001) in their study „Who benefits from foreign direct investment in the UK‟ examined whether there is any productivity or wage gap between foreign and domestic firms using annual data from 4000 domestic firms for the period 1991-1996. The results indicate that domestic firms have lower productivity compared to foreign firms and also that foreign firms pay higher wages. In addition, firms with highly skilled labour and a high degree of international competition benefit positively from FDI whereas foreign firms are detrimental to firms with low skill levels and large technological gaps (low productivity). Da Rin and Hellmann (2002) in their study developed a theory of the role of banks as catalysts for industrialization in emerging economies. The study sought to find out whether banks could affect the equilibrium of the economy as well as it examined the role of banks to create new industries and under which condition would banks serve as a catalyst for economic growth. The 21 University of Ghana http://ugspace.ug.edu.gh results suggest that banks with adequate market power earn profits from coordination and are adequately large to mobilize an important number of firms to enhance industrialization. Despite the banks‟ ability to improve coordinated investments through market power and size, they may prefer concentration in the industrial sector. Dutta and Ahmed (2004), analysed the link between industrial growth and the trade programmes in Pakistan spanning the 1973-1995 period. Employing the cointegration and error correction methods of estimation, the study found evidence of a stable relationship in the long run among human capital, additional value to the industry, stock of capital and export and import tariffs. Barrios, Görg and Strobl (2005), employing a semi-parametric regression technique on panel data from the manufacturing industry in Ireland during the period 1972-2000, examined the influence of FDI on the development of local firms, focussing on the competition effect and positive market spillovers. The evidence from the study indicates that the number of domestic firms depicts a U-shaped curve suggesting a dominance of the competition effect in the initial stages which is gradually outweighed by positive spillovers. Thus, the negative competition effect initially deters the entry of domestic firms whereas the positive externalities are beneficial to the industry resulting in an overall positive spillover of FDI in the country. Javorcik and Spatareanu (2005), using a panel data on Romanian firms covering the 1998-2003 period analysed whether the level of spillovers within the manufacturing industry depends on affiliates of fully foreign owned firms and investment of joint domestic and foreign firms. The results indicate that the positive externalities generated from FDI depend on the ownership structure. The evidence from the study reveals that the adverse competition effect of the FDI flow on foreign-owned investments is limited by the advanced knowledge dissipation within the industry. Further, there are positive externalities generated from the project of joint domestic and 22 University of Ghana http://ugspace.ug.edu.gh foreign firms, but not from subsidiaries of foreign firms. The study recommends improvements within the domestic industry through development strategies that may help domestic producers acquire skills to fulfil requirements of foreign investors. Girma and Gorg (2005) in their study analysed the role of absorptive capacity in determining the contribution of FDI to domestic firms using a quantile regression technique on a panel data for 80 industries in the United Kingdom covering the period 1980-1992. The results indicate that absorptive capacity measured as the difference between Total Factor Productivity (TFP) in an establishment and the maximum TFP in the industry is essential for productivity spillovers. In addition, there is evidence of a U - shaped link between benefits generated from FDI in the region and absorptive capabilities while an inverted U-shape link for externalities outside the region. The study concluded on policies aimed at upgrading the stock of human capital through training and education which will enhance the skills and knowledge of domestic firms. Bwalya (2006) employing the Generalized Method of Moments (GMM) technique on 125 Zambian manufacturing firms for the 1993 to 1995 period, analysed the importance of productivity spillovers of FDI to domestic firms. The study found evidence that, as a foreign presence in the manufacturing industry increases, the productivity of domestic firms‟ decreases suggesting a negative competition effect of FDI inflows. Also, the results show that while there are no significant intra-industry externalities, there is technological transfer from foreign firms in the upstream sector to domestic firms in downstream sectors. Costa and Filippov (2008) analysed the link between FDI and industrial policies in emerging economies. The study focussed on the existing subsidiaries of foreign multinational enterprises rather than on the attraction FDI inflows. The study suggests the need for government in host countries to encourage innovation and development of existing foreign-owned subsidiaries. 23 University of Ghana http://ugspace.ug.edu.gh Keller (2009) analysed the effect of the transfer of technological knowledge on economic performance across the United States (U.S.) firms and industries covering the period 1987 to 1996. The study focussed on MNCs‟ activities and international trade as channels for positive spillovers. The results indicate that trade and multinational enterprises generate technological externalities. The results further indicate a huge impact on the economy in which FDI externalities represent 20 percent of the productivity growth in the U.S. manufacturing industry. Kaya (2010) analysed the impact of globalization on the manufacturing industry in developing economies. The study focussed on the effect of internal and external factors on industrialization in emerging economies. Utilizing data from 64 emerging economies during the period 1980- 2003, the study found evidence that economic development is the most significant determinant of manufacturing employment. In addition, economic globalization through trade has a positive effect on industrialization in developing economies. The results further show that direct investment by foreign firms in emerging economies may be of less significance in the manufacturing industry than trade between companies from developing countries. Waldkirch and Ofosu (2010) examined the effect of foreign presence in the performance of manufacturing industries in Ghana utilizing a panel data set of 200 manufacturing firms during the period 1991-1997. The findings reveal that the entry of foreign companies produces an adverse effect on local companies, but a positive influence on foreign companies. Moreover, the study found no evidence of wage effects. The findings of the study do not indicate that the adverse effect of foreign entry is outweighed by the positive growth effect as indicated in other studies, suggesting that results vary across countries. Szirmai and Verspagen (2011) investigated the role of manufacturing as a channel for growth in developing countries. Using the Hausman Talyor technique in 88 countries spanning the period 24 University of Ghana http://ugspace.ug.edu.gh 1950-2005, the results reveal a positive influence of manufacturing performance on the growth in developing economies with a highly educated labour force which conforms to the engine of growth hypothesis. Further, the study found evidence that the interaction between manufacturing and GDP per capita is negative. The study asserts that a large stock of human capital is needed to generate the positive spillover effects of the manufacturing industry. Doytch and Uctum (2011) employing the GMM technique to examine the impact of FDI on manufacturing and service sector in 60 countries spanning the period 1990-2004. Using variables such as GDP, value-added to the manufacturing and service sector, FDI and financial and non- financial services, the results indicate a positive impact of FDI in the manufacturing industry of developed countries. Further, the findings show that FDI in the financial sector generates a positive spillover in Southern and Eastern Asia, the Pacific and in high-income countries. Xu and Sheng (2012) investigated the effect of externalities emanating from FDI on local companies in the Chinese manufacturing sector covering the period 2000-2003. The findings reveal that FDI contributes to the manufacturing industry initially, and the benefits of FDI diminish as FDI increases and further generate a negative effect at a certain point. The evidence also suggests that FDI contribution to domestic firms varies due to the firm structure as well as the origin of FDI. The study concludes on the role of government in reducing tax incentives aimed at attracting FDI. Naudé, Szirmai and Lavopa (2013) in their study of „The nature of economic development in Brazil, Russia, India, China and South Africa (BRICS)‟ analysed the forms of structural change spanning the period 1980-2010. The findings reveal that three countries did not witness industrialization while China is the only economy where growth in the manufacturing industry constitutes a major part of overall economic growth. 25 University of Ghana http://ugspace.ug.edu.gh Umer (2013), investigated the impact of FDI and trade liberalization on the manufacturing industry using a time series data from Pakistani industries spanning the 1960-2011 period. Employing the co-integration and the vector error correction model, in his estimations, the results indicate that real GDP and foreign direct investment contribute positively to the growth in the manufacturing industry in the long run, while inflation and trade liberalization had an adverse influence on the growth in the manufacturing industry. However, in the short run, inflation and trade liberalization did not yield any effect on growth in the manufacturing industry, while real GDP, foreign direct investment, and real exchange rate contribute positively to the industrial sector. The study concluded on policies aimed at managing capital from local investors to boost industries instead of focusing on foreign direct investment. Beji and Belhadj (2014), examined the main determining factors of industrialization using data from thirty-five African countries during the 1970-2012 period. Employing the system GMM technique, the results reveal that financial development, governance, and regulation of the labour market have an influence on the industry. In addition, the study found evidence that financial and institutional factors mainly determine the industrialization process in the northern and eastern sectors of African countries, while socioeconomic factors determine industrialization in the western and southern countries. Additionally, the exchange rate showed an adverse effect on the industrialization of African countries. In conclusion, the study recommends African leaders to develop a strong financial system to absorb the benefits of financial openness as well as maintain a stable inflation and exchange rates. Gui-Diby and Renard (2015) analysed the link between foreign direct investment and industrial policies using annual data from forty-nine countries in Africa spanning the 1980-2009 period. The results reveal that FDI does not contribute to the industrialization process in Africa. 26 University of Ghana http://ugspace.ug.edu.gh However, the financial sector, international trade, and market size are essential factors that determine industrialization in Africa. The study suggests the need for government to get involved in policies aimed at attracting FDI in order to generate the impact on industrialization, as well as the need for policy makers to formulate and implement useful industrial policies. Guadagno (2016), analysed the determinants of industrialization in 74 countries during 1960- 2005. Using the Cornwall model which explains the growth of the manufacturing output and economic growth, the results indicate that the industrialization process is faster for larger countries with an undeveloped industrial base. Moreover, results reveal that trade openness contributes to the growth of the manufacturing industry, suggesting that trade promotes the expansion of the manufacturing industry rather than stimulates growth in the economy. Contrary to theories of industrialization associated with a low inflation rate, the study found that inflation has a positive influence on industrialization. This study analyses the role of financial market development with the link between FDI and industrialization in SSA during the 1990-2014 period. The study is different from prior research such as Agbloyor et al. (2014) and Gui-Diby and Renard, (2015) because it examines whether financial market development is necessary for FDI inflows to have the desired effect on industrialization as well as takes into account heterogeneity among countries in the sub-Saharan region. Table 3.1 provides a summary on FDI and industrialization. The ambivalent nature of the reviewed literature indicates differences across studies. The variation in findings may be attributed to varying sample periods, countries under study as well as the estimation technique used. 27 University of Ghana http://ugspace.ug.edu.gh Table 3.1: Summary of Empirical Studies on Industrialization and FDI S.No. Author (Year) Period of the study Methodology Empirical findings 1. Li et al. (2001) 1970-1999 3.S.L.S. The magnitude for which spillover occurs differs from the various forms of domestic firms‟ ownership. 2 Barrios et al. (2005) 1972-2000 Semi-parametric The dominance of the competition effect in regression the initial stages is gradually outweighed techniques by positive spillovers. 3. Görg & Strobl 1991-1997 OLS Local enterprises are less productive (2005) compared to local enterprises previously owned by foreigners. 4. Agosin and 1972-2000 GMM FDI does not influence domestic Machado (2005) investment 5. Javorcik and 1998-2003 OLS and FE The positive externalities generated from Spatareanu (2005) FDI depend on the ownership structure. 6. Schneider (2005) 1970-1990. OLS and FE The influence of FDI on economic growth is ambiguous. 7. Hansen and Rand 1970-2000 FE FDI has a long run impact on GDP and (2006) thus they contribute to economic growth. 8. Bwalya (2006) 1993-1995 GMM As foreign presence in the manufacturing industry increases, the productivity of domestic firms‟ decreases. 9. Kasuga (2007) 1980-1999 OLS, FE, and RE The impact of FDI depends on the host country„s income level, financial structure and governance infrastructure. 10. Ndikumana and 1970-2005. FE FDI crowds in domestic investment and Verick (2008) private investment encourage FDI. 11. Adams (2009) 1990-2003 OLS and FE FDI has a negative effect on domestic investment and thus there is a crowding out effect. 12. Azémar and 1985-2005 OLS and FE SSA receives less FDI compared to the rest Desbordes (2009) of the countries in the sample because of lack of public goods, human capital, and health status. 13. Njikam (2009) 1986-1994 and OLS The performance of the manufacturing 1995-2003 industry is based on infrastructural development. 14. Keller (2009) 1987-1996 OLS FDI externalities represent 20 percent of the productivity growth in the U.S. manufacturing industry. 15. Anaman and 1974-2006 Co-integration and The level of the output of the Amponsah (2009) VECM manufacturing sector, in the long run, is influenced by the real GDP per capita and trade openness. 16. Kaya (2010) 1980-2003 FE and RE FDI in emerging economies may be of less significance in the manufacturing industry 17. Doytch and Uctum 1990-2004 GMM, FE, and FDI in the financial sector generates a (2011) OLS positive spillover in Southern and Eastern Asia, Pacific and in high-income countries 18. Szirmai and 1950-2005 Hausman Talyor Large stock of human capital is needed to Verspagen (2011) technique generate the positive spillover effects of the manufacturing industry. 28 University of Ghana http://ugspace.ug.edu.gh 19. Fillat and Woerz 1987-2002 GMM and OLS The effect of FDI on growth in the (2011) economy depends on the developmental stage. 20. Xu and Sheng 2000-2003. RE and GMM FDI contribution to domestic firms varies (2012) due to the firm structure. 21. Anwar and Cooray 1970-2009 OLS, GMM, and Financial development increases the (2012) FE benefits derived from FDI. 22. Kashcheeva (2013) 1970-2009 OLS, GMM, and The study concludes that FDI contributes FE to economic development. 24. Kaur et al (2013) 1991-2010 FE and RE The findings of the study reveal a positive effect of FDI on the banking sector and the stock market capitalization. 25. Umer (2013) 1960-2011 Co-integration and FDI showed a positive and significant VECM impact on the industrial sector. 26. Beji and Belhadj 1970-2012. GMM Financial development has an impact on (2014) industries in Africa. 27. Feeny et al (2014) 1971-2010 OLS and GMM The effect of FDI on economic growth in the sample is lower than the average impact of the host countries. 28. Doytch et al (2014) 1990-2009 GMM FDI in manufacturing and mining decrease child labor. 29. Omri et al (2014) 1990-2011. GMM A bi-directional causality between economic growth and FDI. 30. Gui-Diby and 1980-2009. RE and FE FDI does not contribute to the Renard (2015) industrialization process in Africa. 30. Guadagno (2016) 1960-2005 Cornwall model Industrialization process is faster for larger countries with an undeveloped industrial base. Notes: 3.S.LS., OLS, RE, GMM, and VECM are acronyms for Three Stage Least Squares, Ordinary Least Squares, Random Effect, Fixed Effect, Generalized Method of Moments and Vector Error Correction Model respectively. Source: Author‟s construct 3.4 Chapter Summary The chapter discussed the theoretical literature on Industrialization and Foreign Direct Investment (FDI), trends of FDI in SSA, the theoretical determinants of industrialization and the role of well-developed financial institutions in absorbing the externalities of FDI. The chapter further reviewed the extant literature on the link between Industrialization and FDI. The reviewed literature reveals variation across studies due to alternative techniques employed. 29 University of Ghana http://ugspace.ug.edu.gh CHAPTER FOUR METHODOLOGY 4.1 Introduction This chapter discusses the estimation techniques adopted in exploring the role of financial institutional development in the relationship between FDI and industrialization. The analysis of the study proceeds in two stages. The first stage analysis addresses the first objective of the study, which investigates the effect of both FDI and financial institutional development on industrialisation. The second stage analyses the function of the financial institutions in the relationship between FDI and industrialization by analysing the effect of the interaction term (FDI and financial institutional development) on industrialization. 4.2 Model Specification As indicated earlier, the main objective of the study is to examine the role of financial institutions in the relationship between FDI and industrialization in Sub-Saharan Africa (SSA). The study adopts the model by Gui-Diby and Renard (2015) to analyse the effect of FDI and financial institutions on industrialization. The model employed in this study differs slightly from the former study in that this study focuses on industrialization as the dependent variable whilst the former employs economic growth as the dependent variable. 4.2.1 Static Model Following the study of Gui-Diby and Renard (2015), the basic model is specified as: (1) where denotes industrialization; denotes variables that determine industrialization; denotes inflows of Foreign Direct Investment and denotes financial 30 University of Ghana http://ugspace.ug.edu.gh institution. Further is country specific effect, are parameters to be estimated, denotes time specific effect and is the random error term and i denotes countries at time t. The full static model in equation (1) is specified as: (2) (3) Where denotes industrialization; denotes inflows of Foreign Direct Investment; denotes financial institutions; is the interaction between FDI and financial institutional development; denotes GDP per capita, which measures relative income to the most developed country‟s income; denotes Exports; denotes Inflation; denotes human capital; denotes population size. Further are parameters to be estimated. Equation (2) captures the independent effect of FDI and FM on industrialisation while Equation (3) captures the interactive effect of FDI and FM on industrialisation. 4.2.2 Dynamic Model The model seeks to explain the pattern of industrialization of various countries over a time period utilizing a model specification which permits testing of hypotheses. Considering the dynamic process of industrialization, the study specifies a dynamic model for industrialization which account for previous levels of industrialization (lagged dependent variable). The introduction of this variable in the model specification renders the static model inefficient. To overcome this, the study employs the system GMM. 31 University of Ghana http://ugspace.ug.edu.gh Following the model employed by Baltagi et al. (2009), the dynamic model is specified as: (4) (5) Where denotes industrialization; is the lagged of industrialization which captures the dynamics and the level of industrialization, denotes inflows of Foreign Direct Investment; is the interaction between FDI and financial institutional development; denotes financial institutions; denotes GDP per capita, which measures relative income to the most developed country‟s income; denotes Exports; denotes Inflation; denotes human capital; denotes population size. Further are parameters to be estimated. Similarly, Equation (4) captures the independent effect of FDI and FM on industrialisation while Equation (5) captures the interactive effect of FDI and FM on industrialisation. 4.3 Description of Variables Employed in the Empirical Analysis The adoption of these variables in the empirical model conforms to extant theory and empirical evidence which suggest the inclusion of these variables is essential predictors for industrialization. Drivers of industrialization from theoretical literature are generally three broad drivers. These are the demand side factors (income levels or relative income to the most developed country‟s income to proxy technology gap, population size to proxy size of domestic market, exports to proxy size of external market and real exchange rate that affect the competitive nature of the 32 University of Ghana http://ugspace.ug.edu.gh exports), institutional and macroeconomic factors (inflation, investment, political institution, financial institutions), supply side factors (skills capabilities (human capital) proxy with secondary education, technological capabilities proxy with R&D expenditures) Industrialization (INDU) The dependent variable used in the regression is the additional value to the manufacturing industry as a percentage of GDP. The value added is the net output of the manufacturing sector, which include the addition of all outputs and subtraction of intermediate inputs. It is calculated without the deductions for depreciation of fabricated assets or depletion and degradation of natural resources. Manufacturing refers to industries as defined by the International Standard Industrial Classification (ISIC). Following the studies by Kang and Lee (2011) and Gui-Diby and Renard (2015), the study employed the value added of the manufacturing industry scaled by GDP as a proxy for industrialization. Lagged Industrialization (INDU-1) The level of industrialization depends on previous levels of industrialization hence the study captures this dynamic process of industrialization by including the lagged value of industrialisation. The lagged value of industrialization accounts for catch up or cumulativeness in the industrialisation process. The inclusion of this variable is in line with the study by Gui-Diby and Renard (2015), in which they establish a positive link between lagged industrialisation and industrialization. Foreign Direct Investment (FDI) FDI is defined as a secure investment to develop a long serving management interest of an operating company in the host country with the aim of the investor having an operative role in the management of the company. It is the sum of reinvestment of earnings, equity capital and 33 University of Ghana http://ugspace.ug.edu.gh other short and long-term capital recorded in the balance of payments. In line with the studies by Kaya (2010), Kang and Lee (2011) and Gui-diby and Renard (2015), the inflow of foreign direct investment as divided by GDP was used as a proxy for FDI. Based on the extant evidence, the impact of FDI on industrialization was ambiguous. Relative Gross Domestic Product Per Capita (GDPC) Relative Gross domestic product is the relative income to the most developed country‟s income (US). Gross domestic Product measures the total value of economic activity within an economy over a period of time. Relative GDP per capita was expected to yield a positive relation to industrialization since the level of income or economic development promotes industrialization. Following the studies of Kaya (2010) and Kang and Lee (2011), a positive relationship was expected between industrialization and relative GDP. Population Size The size of population is employed as a proxy for size of the domestic market. The total population constitutes all residents regardless of legal status or citizenship. The inclusion of this variable is based on the studies by (Szirmai & Verspagen, 2015; Guadagno, 2016). Exports (EXP) Exports are measured as the sum of all exported goods and services as a percentage of GDP. Export is expected to generate a positive effect on industrialization. Export is used as a proxy for external market. Based on the extant evidence, the effect of exports on industrialization was ambiguous. 34 University of Ghana http://ugspace.ug.edu.gh Financial institutions development (FM) The main aim of a well-developed financial institution is to promote the capabilities of the financial system. The financial system includes instruments, institutions, and all financial markets. Financial market development enhances the efficiency of financial decisions through the allocation of resources which promote industrialization. Domestic credit provided by the financial sector is used a proxy for Financial institution. Domestic credit provided by the financial sector includes all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net. Inflation (INFL) Inflation is measured as the annual percentage change in the Consumer Price Index (CPI). A stable macroeconomic environment is essential to spur savings, investment and ultimately economic growth. Also, a stable macroeconomic environment helps economic agents in planning and therefore should improve the performance of businesses. Since inflation is used as proxy for macroeconomic stability, a negative relationship is expected between inflation and industrialization. Human Capital Human capital account for skills accumulation and skills capabilities Secondary school enrolment is used as a proxy for Human capital. Net enrolment ratio is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers. 35 University of Ghana http://ugspace.ug.edu.gh In line with Szirmai and Verspagen (2011), a positive link is expected between human capital and industrialization. Table 4.1: Summary Description of the Variables Employed in Regression Variable Definition Measurement Prior Expectation DEPENDENT VARIABLE INDU Industrialization The value added of the manufacturing sector as a percentage of GDP EXPLANATORY VARIABLES INDU-1 Lag of industrialization Lagged of the value added of the + manufacturing sector FDI Foreign Direct Investment Foreign direct investment inflows as a +/- percentage of GDP GDPC Relative Gross domestic Gross domestic product per capita to the + product most developed country‟s income (US) POP Population Total Population EXP Exports Exports as a percentage of GDP +/- FM Financial institution Domestic credit provided by banks + INFL Inflation Annual change in the CPI + FDI*FM Interaction of FDI and FM Interaction between FDI and FM + HC Human Capital Secondary School Enrolment + Source: Author‟s Construct 4.4 Estimation Technique This study employs a panel regression analysis with STATA 13 as the analytical software. Formal regression analysis involves the use of model estimation techniques including Ordinary 36 University of Ghana http://ugspace.ug.edu.gh Least Square Estimation (OLS), Random Effect Model (REM) and Fixed Effect Model (FEM), Instrumental Variable (IV) method, Two-Stage Least Square Estimation, and Generalized Methods of Moments (GMM). The Ordinary Least Square Estimation (OLS) or Pooled regression is the most widely used estimation technique in panel data. It is simple to perform as it does not require the use of any special technique. The OLS assumes that all entities (countries) operate in the same way over a period of time. However, its demerit lies in the fact that it does not consider the differences within entities (countries) as well as variation in time (time effects). The OLS ignores time effect and country heterogeneity which might lead to wrong conclusions. The Random Effect Model (REM) and Fixed Effect Model (FEM) on the other hand take into consideration peculiar differences within entities. Hence the REM and FEM are deemed superior over the OLS since it accounts for both heterogeneities in countries (country-specific effect) and variation in time. Fixed Effect Model (FEM) The fixed effect model explores the link between the predictor and the outcome variables within an entity. It assumes correlation between that country specific effect and the predictors. The FEM assumes that the time-invariant characteristics are unique to the individual and must not be correlated with other individual characteristics. The fixed effect regression technique accounts for omitted variables in a panel model in which the omitted variables are time-invariant but not country-variant. Thus, a fixed effect method controls for omitted variables that vary across countries which do not change over a period, for instance, institutional quality, cultural, historical and managerial differences. In effect, the fixed effect model controls for all time- variant differences among the individual countries so the estimated coefficient of the fixed effect model cannot be biased because of omitted time-invariant characteristics. The fixed effect 37 University of Ghana http://ugspace.ug.edu.gh regression method has different intercepts for each country, which is represented by a binary variable to absorb the influence of all omitted variables that are country-specific and time- invariant. The introduction of a country-specific, time-invariant variable would produce a fixed effects regression model as: (6) The presence of unobserved country-specific and time-invariant variables causes the endogeneity problem and biases the estimated coefficients hence the FEM seeks to resolve the problem through the process of an „entity demeaned‟ OLS algorithm. The process uses two steps to address the endogeneity problem by subtracting the average country-specific effect from each variable after which the regression is estimated using the entity demeaned variables. Taking the average country-specific effects ( from both sides of equation (6) yields: (7) Where , are the country-demeaned variables that are used in the fixed effects estimation model. The process hence eliminates the country-specific effect from the model, and the mean value of the error term ( ) remains the same as the actual value of the country-specific error terms ( ), since the error term does not change over time. The FEM estimation technique assumes heterogeneity in the error term across countries and is hence suitable to deal with heteroscedasticity in panel regression estimation. Unlike the pooled OLS estimation technique, the fixed effect estimation addresses the omitted variable bias by controlling for fixed effects, but has the tendency of compounding the problem of measurement error (Hauk & Wacziarg, 2009). 38 University of Ghana http://ugspace.ug.edu.gh Random Effect Model (REM) The Random Effects Model (REM), on the other hand, assumes no correlation between unobserved country-specific, time-invariant and the regressors. Thus, it assumes that variation across entities is random and not correlated with the predictor or the independent variables in the model. The Random effect model postulates that although country-specific, time-invariant and the explanatory variables are uncorrelated, the influence of such unobserved variables must be specified in the regression model. The REM, therefore, uses all available data, produces unbiased parameter estimate and smallest standard error, but the unobserved country-specific, time-invariant variable would produce omitted variable bias. An advantage of the REM over the FEM is that it includes time invariant variables such as gender. Under the FEM, the time invariant variables are absorbed by the intercept. In choosing between the FEM and the REM, the Hausman test is employed. The Hausman test tests whether the country specific effect is correlated with the regressors. The null hypothesis states that there is no correlation between the country specific effect and the regressors and the alternate hypothesis states that there is. In other words, the null hypothesis states that the preferred model is the random effect model and the alternative is in favour of the fixed effect model. Generalised Method of Moment (GMM) However, in panel regression analysis, bias exists if the explanatory included a lag dependent variable. This bias may render coefficient estimates inconsistent in different techniques. The System GMM estimation technique would be employed in estimating the dynamic panel model so as to deal with any possible biases due to the dynamic structure. Literature has identified two 39 University of Ghana http://ugspace.ug.edu.gh major estimation techniques as an effective tool for the GMM technique, namely, the Instrumental Variable (IV) and Two-Stage Least Square Estimation (2SLS) methods which have a weakness as they use “external” instrument. These methods use “external” variables as instruments to correct for potential endogeneity among variables, however, those instruments hardly meet the condition of “validity and relevance” of a good instrument and are usually weak. Thus, both IV and 2SLS techniques rely on obtaining another variable that is correlated with the regressor causing endogeneity, but not correlated with the random error term. It is however difficult to satisfy exogeneity and relevance property of a good instrument when using an external instrument because one could hardly find an instrument that is correlated with the regressor and at the same time not correlated with the random error term. Baum et al. (2003) stated that in the presence of heteroscedasticity, the GMM estimation technique yields more efficient estimates than the 2SLS and the IV. If errors are heteroscedastic, the class of instrumental variable estimators that use a linear combination of the instrument no longer becomes efficient, but the efficient estimator is the Generalize Methods of Moments (Stock & Watson, 2007). The GMM technique uses lags of endogenous variables as instruments; in which the endogenous variables are predetermined hence are not correlated with the error term. Generally, GMM technique produces consistent and efficient estimates of parameters in view of the following characteristics within the data generating process:  When instruments employed to deal with the presence of endogeneity among some variables are lags of the explained regressors. However, the validity of the instruments depends on the source (variable) of endogeneity.  The data sample contains small time periods and large entities (countries).  There exist country-specific fixed effects which are randomly distributed. 40 University of Ghana http://ugspace.ug.edu.gh  No autocorrelation across countries, but with country-specific autocorrelation and heteroscedasticity in the error term.  When lagged dependent variable influences the dependent variable. There are two forms of GMM estimators identified in literature, namely, the Difference GMM and System GMM. The difference GMM presented by Arellano and Bond (1991) seeks to solve the problem of inconsistency as a result of endogeneity among some variables in the model by using the first difference of the equation being estimated. The difference equation is expressed as: (8) Equation (8) hence eliminates country-specific effect, thereby resolving inconsistency and biases due to endogeneity by using lags of endogenous variables as instruments. The difference GMM technique hinges on moment condition with the assumption of weak exogeneity of regressors and no serial correlation respectively specified in the equations below: (9) (10) Although the difference GMM helps solve endogeneity among variables, it has some limitations. The process out rightly eliminates time-invariant country-specific effect which may be of interest leading to model misspecification. The difference estimator may suffer weak instrument problem when the dependent variable is highly persistent given that the difference method poses some serious biases. Weak instrument undermines the asymptotic properties of the differenced estimator and may be harmful for small sample resulting in the increased variance of the coefficient and biases the coefficient of the small samples. 41 University of Ghana http://ugspace.ug.edu.gh The system GMM technique as designed by Arellano and Bover (1995) and Blundell and Bond (1998) seek to address issues of weak instrument associated with the difference GMM technique using level equation and differenced equation. The efficiency of the equation under estimation is improved if moment conditions of its level form and the differenced forms are combined (Roodman, 2009). The system GMM is designed within additional moments condition specified as: (11) (12) The lagged differences are employed as instruments for endogenous variables in the level equations since these values become the suitable instruments in view of additional moment conditions. The additional moment conditions are based on the assumptions that there may be correlation between the country-specific fixed effect and the predetermined variables in the equation, as well as no correlation between lagged differences and country specific fixed effects. The system GMM is considered the most appropriate panel regression estimation technique due to the following characteristics inherent in its process:  It resolves the endogeneity problem by the adoption of lagged values of regressors as instruments.  It allows the use of level and lagged values of the variables used in the equation under estimation.  The problem of information loss associated with cross-sectional regression is eliminated since the system GMM makes use of multiple observations for each entity (country) across time. 42 University of Ghana http://ugspace.ug.edu.gh  System GMM is able to produce a consistent and unbiased estimate of parameters even with a small time period (T) and large entities (N). According to theory, dynamic panel GMM estimator solves the problems of endogeneity, omitted variables bias, measurement error within the panel OLS estimation, but portrays weak instrument problem (Bazzi & Clemens, 2009; Roodman, 2009). Hauk and Wacziarg (2009) claim that system GMM is the most preferred technique despite that it sometimes exhibits weak instrument drawback. In order to confirm that the system GMM produces a valid estimate of the model, some additional diagnostic tests are conducted. These tests include autocorrelation test and test for validity of over-identifying condition. The GMM estimation requires the following conditions. First, the null hypothesis of no second auto correlation using the second order auto correlation test by Arellano and Bond must not be rejected, suggesting that, there is no serial correlation within the error term. Second, the null hypothesis test of over identifying restrictions using the Hansen test must also not be rejected indicating that the validity of instruments employed in the GMM estimation. Using the second order auto correlation, and Hansen test, a suitable set of instruments is selected for GMM estimation. 4.5 Diagnostics Tests This study undertakes some diagnostic tests to ensure that the estimated model does not suffer any biases within the panel regression analysis. Pre-estimation tests such as multicollinearity autocorrelation, heteroscedasticity, and endogeneity test were conducted in order to ensure general aptness of the model and variables. 43 University of Ghana http://ugspace.ug.edu.gh 4.5.1 Multicollinearity Multicollinearity is said to exist in a linear regression model where there is a correlation among the explanatory variables. According to Cohen (1993), in a model specification, explanatory variables that are highly correlated should not be used in the same model because highly correlated variables yield the same effect. The inclusion of highly correlated variables in the same model may lead regression estimates to be biased. A Variance Inflation Factor (VIF) test was conducted to check for collinearity among independent variables. 4.5.2 Heteroscedasticity and Autocorrelation One of the assumptions underlying Ordinary Least Squares (OLS) is homoscedasticity, thus constancy of variance of the error term, but within a panel regression analysis such assumption breaks down as a result of the presence of unobserved variables that varies across countries but constant over time within panel data. Heteroscedasticity is said to occur when the variance of the unobservable error , given the independent variable is not constant. The variance which is a function of independent variables may be specified as: (13) Although heteroscedasticity does not affect the unbiasedness of OLS, it renders parameters and variance inefficient. The study employs the Breusch –Pagan/Cook-Weisberg test to check for the presence of heteroscedasticity. Autocorrelation refers to a situation where the random error term is correlated over time for a given entity. The error terms are said to be auto-correlated if, and only if, Cov (ui, uj) ≠ 0, for i ≠ j. Autocorrelation is caused by model misspecification, data manipulation, event inertia, and spatial ordering. Secondly, the presence of country-specific, time-invariant effect, longitudinal data may result in the issue of autocorrelation within the model. Individual countries may have 44 University of Ghana http://ugspace.ug.edu.gh distinctive management and operational features which need to be considered in the estimation process. The presence of these sources of persistence indicates the existence of serial correlation that would render the OLS parameter estimates not to be the Best Linear Unbiased Estimator (BLUE). The study employs the Wooldridge‟s test of autocorrelation for panel data sets to check for autocorrelation. For likelihood of autocorrelation and heteroscedasticity within panel data, the robust one-step estimate of the standard error was employed. 4.5.3 Endogeneity Endogeneity is said to occur in a model when a correlation exists between regressors and the error term. Thus Cov (Xj, uit) ≠ 0 for some j = 1,…,k. The endogeneity problem is caused by an omitted variable, measurement error, and reverse causality. Omitted variable bias occurs when OLS is applied to a regression model that excludes a key variable due to data unavailability and the excluded variable has a correlation with one of the explanatory variables and in part determines the dependent variable. Measurement error often arises because of reporting and/or coding errors. Reverse causality occurs in a model when there is a reversed causal link between exogenous and endogenous variables. The study seeks to analyse the role the financial institutions play in the link between FDI and industrialization. However, there may be a reverse causality between Industrialization and FDI. In order to confirm endogeneity in the model, a Durbin–Wu–Hausman (DWH) test for endogeneity was conducted for the residuals to establish the existence or otherwise of endogeneity among variables. 45 University of Ghana http://ugspace.ug.edu.gh 4.6 Data Sources and Sample Size The study used a panel model with data culled from African Development Indicators (ADI) and World Development Indicators (WDI). Data on macroeconomic variables such as gross domestic product per capita and inflation were sourced from the World Development Indicators. The sample for this study was drawn from 45 Sub-Saharan Africa (SSA) countries for the period 1990 to 2009. The Forty-five (45) SSA countries comprise sixteen (16) Western African countries, ten (10) Eastern Africa, ten (10) Central Africa and nine (9) Southern African countries. The study relied on data that were available for each country during the study period. 4.7 Chapter Summary The chapter discussed the estimation techniques and empirical model in examining the effect of FDI and financial institutions development on industrialization. It further analysed the role of financial institutions development in influencing FDI to enhance industrialization by interacting FDI and financial institutions development. 46 University of Ghana http://ugspace.ug.edu.gh CHAPTER FIVE RESULTS AND DISCUSSION OF FINDINGS 5.1 Introduction This chapter presents and discusses the findings of the study. It analyses and provides descriptive statistics of the variables employed in the analysis. As indicated earlier, the first stage analysis examines the effect of both FDI and financial institutions on industrialization. The chapter further provides results of the second stage analysis, which analyses the interaction between FDI and financial institutions to assess financial institutions influence on FDI inflows to enhance industrialization. 5.2 Descriptive Statistics Table 5.1 provides the summary of the descriptive of variables employed in the empirical model. The summary statistics provides a general description of variables employed in the regression model. The salient descriptive estimations are the number of observations, standard deviation, mean and the minimum and maximum values of the variables over the period under consideration. As shown in Table 5.1, some of the regressors exhibit high levels of variation which include inflation (INFL) and Exports (EXP). The volatility in these variables may be attributed to variation in time. The standard deviation of GDPC is quite high which attest to the heterogeneity in the sample. 47 University of Ghana http://ugspace.ug.edu.gh Table 5.1: Summary Descriptive Variable Mean Std. Dev. Min Max Obs lnINDU 2.177 0.614 0.361 3.744 864 LLINDU 2.183 0.613 0.361 3.743 827 lnFDI 4.468 0.179 0.000 5.434 882 GDPC 0.081 0.120 0.004 0.696 897 lnPOP 15.516 1.507 11.156 18.860 900 lnEXP 3.254 0.655 0.666 4.622 875 lnFM 4.640 0.377 0.000 8.087 850 lnHC 3.232 0.732 1.636 4.753 544 lnINFL 3.951 0.641 0.000 10.104 805 Source: Author‟s construct Further, the mean of FDI inflows as a percentage of Gross Domestic Product (GDP) is 4.47 percent, which is low. The mean of relative GDP per capita (GDPC) which captures the relative income of SSA countries to the most developed country‟s income (US) is 8.1 percent. The mean value of for exports (EXP) as percentage of GDP is 3.25. Also, the mean value of the logarithm of population size (POP) which indicates the size of the domestic market is 15.52. Additionally, the mean for Human capital (HC) is 3.23. Human capital (education) captures the skills capabilities. With regard to the financial institution, as indicated by the domestic credit provided by the financial sector (FM), the development of the financial system in SSA on average is 4.6 percent of GDP. Finally, the mean of inflation of the SSA countries during the study period is 3.91. 5.3 Diagnostic Tests Results In order to ensure that the model does not suffer from any bias and inconsistent estimates, some diagnostic tests were carried out to ensure unbiased, reliable and consistent estimates. The diagnostic tests carried out in the study include multicollinearity, autocorrelation, heteroscedasticity and endogeneity. 48 University of Ghana http://ugspace.ug.edu.gh 5.3.1 Multicollinearity Results Appendices C and D depict the correlation matrix and Variance Inflation Factor (VIF) results of the variables employed in the empirical model. As shown in Appendix C, some of the regressors are highly correlated with each other with their values exceeding 0.50. For instance, the highest correlation in the matrix is between GDPC and the population size (POP). However, with the mean Variance Inflation Factor (as shown in Appendix D) of these independent variables being less than 10, the collinearity among the variables can be ignored. 5.3.2 Auto Correlation and Heteroscedasticity Results The tests conducted on the panel model reveal the presence of auto correlation and heteroscedasticity in most of the regressions. Appendices E and F show the test results for autocorrelation and heteroscedasticity respectively. The Breusch-Pagan / Cook-Weisberg test is used to test for heteroscedasticity while the Wooldridge test is employed for autocorrelation. As shown in the Appendix F the null hypothesis states that there is a constant variance which suggest that the variance of the error term is equal (homoscedastic). With the P-value of 0.0000 being less than the significant level of 0.05, the study rejects the null hypothesis and accepts the alternative which states the presence of heteroscedasticity. The robust standard error is applied to address the presence of heteroscedasticity. As indicated in the Appendix E the null hypothesis states that there is a no first-order autocorrelation which suggest that the error term is not serially correlated with each other. With the P-value of 0.0000 being less than the 5 percent significant level, the study rejects the null hypothesis and accepts the alternative which states the presence of autocorrelation. The robust standard error is also applied to address the presence of autocorrelation. 49 University of Ghana http://ugspace.ug.edu.gh 5.4 Empirical Results The empirical results were in two folds. The results in Table 5.2 captures the first objective of the study which is to examine the effect of FDI and financial intuitions on industrialisation while that of Table 5.3 addresses the second objective of the study which is to assess the interactive effect between FDI and financial institution on industrialisation. In all, there were four different regressions presented in each Table. The regressions comprised of a static model (OLS, FEM and REM) and a dynamic model (GMM). With regards to the static model, the use of the Random Effect Model (REM) and Fixed Effect Model (FEM) was premised on the fact that it accounts for both heterogeneities in countries and variation in time (country-specific and time effects). Based on the Hausman test as shown in appendices A and B, the FEM was the appropriate model in the static model regressions. The null hypothesis states that the differences in coefficients are not systematic which implies that there is no correlation between the country- specific effect and the regressors. Since the P-values of 0.000 are less than the significant level of 0.05, the study rejects the null hypothesis, hence the use of FEM over REM. In relation to the dynamic model (GMM) the p-value of Arellano-Bond test statistics for the second order serial correlation AR (2) in the residuals fails to reject the null hypothesis of no presence of second-order auto correlation within the error term since the p-values of AR (2) are insignificant. Accordingly, the Hansen test of over-identification reports that the instruments used in the system GMM estimation are valid. The estimated model using the GMM estimator 2 indicates that the Wald chi is highly significant at the 1% level. This suggests that overall the estimated model is significant. The system GMM estimator (dynamic model) is employed in addition to the Fixed Effects Model (static model) to assess the robustness of the static model. Additionally, the system GMM 50 University of Ghana http://ugspace.ug.edu.gh model addresses the problem of endogeneity within the model due to the endogenous nature of some of the explanatory variables. Table 5.2 shows the regression model of the independent effect of FDI and the financial institution on industrialisation. As shown in Table 5.2, the lagged of the valued added of the manufacturing sector has a positive and significant relationship (Model 4). The result suggests that, previous year output of the manufacturing industry is very essential to the industrialization process in SSA. This implies that the industrialization process tends to persist from year to year and that previous level of industrialization increases the current year value-added of the manufacturing industry. The result is in line with the study by Gui-diby and Renard (2015) which found lagged industrialization to have a positive and significant effect on the current year industrialization. The results in the model (4) further show that FDI has a significant but negative link with the output of the manufacturing sector. This result implies that FDI inflow has an adverse effect on the value added of the manufacturing sector. The negative effect may be attributed to the channel through which FDI flows in SSA. Adjasi et al. (2012), posit that FDI inflow in SSA mainly goes into natural resources (mainly oil) with little or no linkages with the domestic economy. The adverse linkage may also be explained by the inefficiency of government to generate a conductive atmosphere for FDI inflows into the manufacturing industry. The results in model (4) also indicate that the financial institution variable which is indicated by the domestic credit provided by the financial sector has a positive but insignificant link with the output level of the manufacturing industry in SSA. The result suggests that financial market development does not play a key role in the industrialization process of SSA countries. This unexpected result may be attributed to the developing nature of the financial institutions (banking sector) in SSA. 51 University of Ghana http://ugspace.ug.edu.gh Furthermore, the result in the static model (3) shows that GDPC as measured by the income level of SSA countries relative to the advanced country (US) has a significant and positive effect on industrialization. The findings suggest that as the income level of household increases, they tend to focus more on manufactured goods. However, under the dynamic model in Model (4), the technological gap between SSA countries and the advanced country has no effect on the output level of the manufacturing in SSA. Additionally, the results indicate a positive and significant relationship between exports and industrialization in model (4). This result suggests that an increase in exports leads to the increase in the valued added of the manufacturing industry. This result is in line with the study by Barua and Chakraborty (2006), which found evidence that trade openness fosters competition, which enhances efficiency and transfer of knowledge. Also, population size (POP) which serves a proxy for the size of domestic market has no effect on the industrialisation process in SSA. Regarding inflation, the results in model (3 and 4) reveal a negative but insignificant effect on the valued added of the manufacturing industry. Finally, Human capital which serves as proxy for skill capabilities depicts a negative but insignificant impact on industrialization (model 3 and 4). The unexpected results imply that, the level of human capital with regards to the training of the labour force is inadequate to promote industrialization in SSA. The result is consistent with the study by Belhadj and Beji (2014) on the determinants of industrialization in SSA but contrary to the study by Guadagno (2016) which found evidence of a positive effect of Human capital (education) on the output level of the manufacturing industry in SSA. 52 University of Ghana http://ugspace.ug.edu.gh Table 5.2: The Independent Effect of FDI and Financial Institutions on Industrialization Dependent Variables INDU INDU INDU INDU OLS (1) REM (2) FEM (3) GMM (4) Explanatory Variables LLINDU 0.928*** (0.0385) lnFDI -0.952*** 0.1 18 0.2 05 -0.666* (0.317) (0.173) (0.207) (0.349) GDPC -0.934*** 2.047*** 2.195*** -0.263 (0.355) (0.654) (0.707) (0.538) lnPOP -0.0720** -0.00705 -0.331 -0.0703 (0.0304) (0.0849) (0.294) (0.0615) lnEXP 0.0617 0.0219 0.0611 0.0675* (0.0750) (0.0696) (0.0795) (0.0401) lnFM 0.430*** -0.0137 -0.0224 0.0301 (0.106) (0.0541) (0.0534) (0.0406) lnHC 0.126** -0.217** -0.118 -0.0843 (0.0567) (0.0997) (0.165) (0.0854) lnINFL -0.0823* -0.0674 -0.0831 -0.00551 (0.0495) (0.0584) (0.0617) (0.0373) Constant 5.467*** 2.561 6.949 4.184*** (1.403) (1.764) (4.149) (1.608) Observations 440 440 440 413 R-squared 0.140 0.184 0.197 Prob > chi2 0.000 0.0 00 Wald chi2 23.06 1492.55 Prob > F 0.000 0.0 00 F-Statistics 11.38 4.07 Arellano–Bond [AR (2), Prob > Z] 0.3 14 Hansen test (Prob > Chi –squared 0.529 Note: Robust standard error in parentheses. *, **, and *** correspond to 10%, 5% and 1% significance, respectively. Based on the Hausman test the FEM is employed for the static model. The endogenous variables captured in the dynamic model are lnFDI, lnPOP, lnFM and lnHC. Source: Author‟s computation using STATA 13. 53 University of Ghana http://ugspace.ug.edu.gh Table 5.3 shows the regression model of the interactive effect between FDI and the financial institution on industrialisation. To ensure that the interaction term does not serve as a proxy for the financial institution indicator and FDI, both FDI and the financial institution variables are independently added to the empirical model. The lagged of the valued added of the manufacturing sector has a positive and significant relationship (Model 8) as previously indicated in Table 5.2. This result suggests that, previous year output of the manufacturing industry is very essential to the industrialization process in SSA. Additionally, the results in Model 7 and 8 further reveal that FDI (the unconditional effect of FDI) has a significant but negative link with the output of the manufacturing sector as previously indicated in Table 5.2. The results also indicate that the financial institution (the unconditional effect of financial institution) variable as shown in both Model (7 and 8) suggest a significant but negative relation with the output level of the manufacturing industry in SSA. However, the conditional effect of FDI on industrialisation is positive and significant. Using the coefficients of FDI, FM and the mean of FM in Model 7, the conditional effect of FDI on industrialisation based on the coefficients is as follows; Industrialisation = -9.284+ 3.520(FM) = 7.04 using the FM value (mean) of 4.640. Thus, the conditional effect of FDI on industrialisation is positive. This suggests that financial institutions enhance the effect of FDI on industrialisation. Thus, FDI is more productive in the presence of well-functioning local financial markets. This may be attributed to the financial institutions ability to serve as a catalyst to the industrial process through absorption of FDI inflows into the manufacturing industry. This result is in line with the study of (Adjasi et al., 2012). 54 University of Ghana http://ugspace.ug.edu.gh Similarly, the conditional effect of FM on industrialisation is significant and positive. Using the coefficients of FDI, FM and the mean of FDI in Model 7 and 8, the conditional effect of FM on industrialisation is positive. Industrialisation = -9.378+ 3.520(FDI) = 6.349 using the FDI value (mean) of 4.468. 55 University of Ghana http://ugspace.ug.edu.gh Table 5.3: The Interactive Effect between FDI and Financial Institutions on Industrialization Dependent Variables INDU INDU INDU INDU OLS (5) REM (6) FEM (7) GMM (8) Explanatory Variables LLINDU 0.938*** (0.0424) lnFDI 14.81** -8.48 4** -9.28 4** -16.77* (7.531) (4.066) (4.173) (8.648) GDPC -0.902** 2.266*** 2.458*** 0.375 (0.358) (0.700) (0.744) (0.705) lnFDIlnFM -3.491** 1.906** 2.103** 3.520* (1.688) (0.882) (0.905) (1.802) lnPOP -0.0808*** 0.00142 -0.329 -0.00440 (0.0305) (0.0873) (0.295) (0.0317) lnEXP 0.0449 0.0225 0.0624 0.123 (0.0744) (0.0676) (0.0776) (0.118) lnFM 15.98** -8.497** -9.378** -15.63* (7.507) (3.900) (3.998) (7.991) lnHC 0.136** -0.226** -0.124 -0.170 (0.0569) (0.100) (0.164) (0.235) lnINFL -0.0856* -0.0652 -0.0809 0.00797 (0.0505) (0.0536) (0.0565) (0.0511) Constant -64.54* 40.71** 49.12** 74.79* (33.39) (18.05) (18.83) (38.56) Observations 440 440 440 413 R-squared 0.148 0.201 0.215 Prob > chi2 0.000 0.0 00 Wald chi2 28.69 1362.1 Prob > F 0.000 0.0 00 F-Statistics 9.3 4.7 Arellano–Bond [AR(2), Prob > Z] 0.3 39 Hansen test (Prob > Chi –squared 0.675 Note: Robust standard error in parentheses. *, **, and *** correspond to 10%, 5% and 1% significance, respectively. Based on the Hausman test the FEM is employed for the static model. The endogenous variables captured in the dynamic model are lnFDI, lnFDIlnFM and lnHC. Source: Author‟s computation using STATA 13. Furthermore, the result in model (7) shows that GDPC has a significant and positive effect on industrialization as previously indicated in Table 5.2. The findings suggest that as the income level of household increases, they tend to focus more on manufactured goods. Additionally, the results indicate a positive but insignificant relationship between exports and industrialization (model 7 and 8). Also, population size (POP) which serves a proxy for the size of domestic 56 University of Ghana http://ugspace.ug.edu.gh market has no effect on the industrialisation process in SSA as previously indicated in Table 5.2. Regarding inflation, the results in the dynamic model (8) reveal an insignificant effect on the valued added of the manufacturing industry as previously indicated in Table 5.2. Finally, Human capital which serves as proxy for skill capabilities (7) depicts a negative and insignificant impact on industrialization previously indicated in Table 5.2. 5.5 Chapter Summary The chapter presented the link between FDI and industrialization in SSA as well as the role of the financial institutions on industrialization. The study employed the static model (FEM) and dynamic model (GMM). The system GMM estimator (dynamic model) was employed in addition to the Fixed Effects Model (static model) to assess the robustness of the static model. 57 University of Ghana http://ugspace.ug.edu.gh CHAPTER SIX SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS 6.1 Introduction This chapter concludes the study by providing a summary of the entire study. It further offers policy recommendations based on the findings of the study. It discusses the limitations encountered in the course of this study as well as suggestions for future research. 6.2 Summary of Findings Based on the presentation and discussion of results, the findings of the study include the following:  The lagged of the valued added of the manufacturing sector has a positive and significant relationship. This result suggests that, previous year output of the manufacturing industry is very essential to the industrialization process in SSA.  The results show that FDI has a significant but negative link with the output of the manufacturing sector which may be attributed to channel of FDI inflow in SSA.  The findings of the study also suggest that the financial institutions have a negative and significant link with the output level of the manufacturing industry in SSA.  However, when FDI is interacted with financial institution, it shows a positive effect on industrialisation. Thus, FDI is more productive in the presence of well-functioning local financial markets.  The results also show that the relative Gross Domestic Product per capita (GDPC) has a significant and positive effect on industrialization which suggests that as the income level of households‟ increases, they tend to focus more on manufactured goods. 58 University of Ghana http://ugspace.ug.edu.gh  The findings indicate that the domestic market (population) does not have a significant relationship with industrialization.  Regarding inflation, the results reveal a positive but insignificant effect on the valued added of the manufacturing industry.  Finally, the evidence indicates that Human capital has no significant effect on industrialization. The results imply that, the level of human capital with regards to the training of the labour force is inadequate to promote industrialization in SSA. 6.3 Conclusion Industrialization is an ongoing process in Africa and in developing countries generally. In 2013, the value-added share of the manufacturing industry in Sub-Saharan Africa (SSA) recorded an average rate of 11 percent which is similar to that of the 1990s. During the same year, the share of worldwide FDI flows into SSA decreased steadily. As part of the industrialization process, financial markets perform an essential role in allocating resources (FDI flows) towards productive investments. The objective of the study was to analyse the role of financial institutions development in the link between FDI and industrialization in SSA. In achieving this aim, the specific objectives were: to examine the effect of FDI inflows and financial institutions on industrialization in SSA and to analyse the role of financial institutions development in determining FDI impact on industrialization in SSA. The main contribution of this study is that, first; it investigated the effect of FDI on industrialization. The rationale behind this objective was to assess whether policies aimed at attracting FDI inflows have been incorporated in industrial policies. Second, it examined how 59 University of Ghana http://ugspace.ug.edu.gh FDI inflows influence industrialization by considering the role of financial institutional development. The study used a panel model with data obtained from the African Development Index (ADI) and World Development Indicators (WDI) of 45 selected countries from SSA spanning the period 1990 to 2009. The analysis of the study proceeded in two stages using both the static model (Fixed Effect Model) and the dynamic model (System Generalised Method of Moment). The dynamic model was employed in addition to the static model to assess the robustness of the static model as well as address the problem of endogeneity within the model. In addition, some diagnostic tests such as multicollinearity, heteroscedasticity and autocorrelation were carried out to ensure reliable, consistent and efficient estimates. The findings of the study reveal that the current level of industrialization also depends on previous levels of industrialization. The results also show that FDI has a significant but adverse effect on the output of the manufacturing sector due to the channel of FDI inflow in SSA, which mainly goes into natural resources (mainly oil) with little or no linkages with the domestic economy. Further, the results indicate that financial institutions play a key role in influencing the effect of FDI on industrialization. The results also show that the relative Gross Domestic Product (GDPC) has a significant and positive effect on industrialization suggesting that the income level contribute to the value of the manufacturing sector in SSA. Finally, the evidence indicates that Human capital has no significant effect on industrialization. The results imply that, the level of human capital with regards to the training of the labour force is inadequate to promote industrialization in SSA. 60 University of Ghana http://ugspace.ug.edu.gh 6.4 Policy Recommendations Based on the findings of this study, the following recommendations are made for policy considerations.  The study‟s finding of FDI inflows having adverse effect on the level of outputs of the manufacturing industry suggests the need for policies aimed at attracting FDI. Thus, governments should provide a conducive environment for FDI to flow into the manufacturing industry. To enhance industrialization, SSA countries should implement more measures to streamline FDI inflows as well as formulate and implement important industrial policies.  Regarding the impact of the financial institutions, governments should strengthen and further develop the domestic financial system to channel resources efficiently into the manufacturing industry. There should be policies aimed at improving the banking sector. To promote effective industrialization, SSA countries should improve the resilience of their financial system in order to positively intermediate FDI inflows into the manufacturing industry.  In summary, policies aimed at improving FDI, improving financial institutions, liberalizing trade, improving income levels as well as the development of the economy will be beneficial for industrialization in SSA. 6.5 Suggestions for Future Research  This study used a short panel data set to examine the role financial institutions play in the link between FDI and industrialization in SSA and as such may underestimate the relationship between FDI and industrialization with a significant time lag. In this context, increasing the time series component of the data may improve the estimates. 61 University of Ghana http://ugspace.ug.edu.gh  The study did not categorize SSA countries into their various sub-region. An analysis based on the decomposition of SSA countries could provide as a robustness check, which will be useful to the extant literature. 6.6 Limitations of the study This study considers 45 SSA countries spanning the period 1990-2009. The study acknowledges that there are other variables that affect industrialization, which were not considered in the study. Moreover, by analysing 45 countries in the same dataset, it was assumed that all countries intended to develop their economies through industrialization, which may not have actually been the case. These limitations notwithstanding, the main findings in this study are reasonably consistent with much of the few studies on FDI and industrialization. 62 University of Ghana http://ugspace.ug.edu.gh REFERENCES Adams, S. (2009). Foreign direct investment, domestic investment, and economic growth in Sub- Saharan Africa. Journal of Policy Modeling, 31(6), 939-949. Adjasi, C., Abor, J., Osei, K. A., & Nyavor-Foli, E. E. (2012). 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Cátedra de Cooperación Internacional y con Iberoamérica (COIBA), Universidad de Cantabria. 70 University of Ghana http://ugspace.ug.edu.gh APPENDICES Appendix A: Hausman Test -Independent Effect of FDI and Financial Institution 71 University of Ghana http://ugspace.ug.edu.gh Appendix B: Hausman Test -Interactive Effect between FDI and Financial Institution Appendix C: Correlation Matrix lnINDU lnFDI GDPC lnPOP lnEXP lnFM lnHC lnINFL lnINDU 1 lnFDI -0.13 1 GDPC 0.06 0.06 1 lnPOP -0.13 -0.09 -0.63 1 lnEXP 0.09 0.21 0.33 -0.29 1 lnFM 0.27 -0.02 0.11 0.01 -0.06 1 lnHC 0.20 0.03 0.59 -0.40 0.55 0.20 1 lnINFL -0.06 0.21 -0.10 0.08 0.07 0.05 0.02 1 72 University of Ghana http://ugspace.ug.edu.gh Appendix D: Variance Inflation Factor Variable VIF 1/VIF GDPC 2.27 0.440859 lnHC 2.15 0.464544 lnPOP 1.8 0.555008 lnEXP 1.59 0.628828 lnFM 1.19 0.841355 LLINDU 1.17 0.851277 lnFDI 1.14 0.874572 lnINFL 1.08 0.923744 Mean VIF 1.55 Appendix E: Autocorrelation Test Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 34) = 148.805 Prob > F = 0.0000 Appendix F: Heteroscedasticity Test Breusch-Pagan / Cook-Weisberg test for heteroscedasticity Ho: Constant variance Variables: fitted values of lnINDU chi2(1) = 54.69 Prob > chi2 = 0.0000 73 University of Ghana http://ugspace.ug.edu.gh Appendix G: List of countries by sub-region Eastern Africa Central African Republic Southern Africa Western Africa Burundi Angola Botswana Benin Ethiopia Cameroon Lesotho Burkina Faso Kenya Central African Republic Namibia Cape Verde Madagascar Congo Swaziland Cote d'Ivoire Malawi Dem. Rep. of Congo South Africa Gambia Mauritius Gabon Zambia Ghana Mozambique Seychelles Zimbabwe Guinea Rwanda Eritrea Comoros Guinea-Bissau Tanzania Equatorial Guinea Chad Liberia Uganda Djibouti Mali Mauritania Niger Nigeria Senegal Sierra Leone Togo 74 University of Ghana http://ugspace.ug.edu.gh Appendix H: Data Country Name YEAR INDU FDI EXP POP HC DCFS INFL GDPC Angola 1990 5.002 -3.263 38.913 11127870 10.370 0.134 Angola 1991 6.236 5.450 30.263 11472173 11.005 83.608 0.126 Angola 1992 4.956 4.982 68.791 11848971 12.426 299.061 0.109 Angola 1993 5.654 5.716 53.901 12246786 1379.414 0.077 Angola 1994 4.942 4.195 85.358 12648483 948.811 0.073 Angola 1995 4.008 9.374 13042666 57.886 2671.792 0.076 Angola 1996 3.446 2.399 82.722 13424813 4.774 4145.108 0.079 Angola 1997 4.378 9.231 68.492 13801868 10.027 219.177 0.078 Angola 1998 6.319 122.790 56.723 14187710 12.028 17.736 107.285 0.078 Angola 1999 3.223 144.516 86.296 14601983 12.024 7.220 248.196 0.074 Angola 2000 2.890 21.142 89.626 15058638 13.779 -14.755 324.997 0.071 Angola 2001 3.873 47.381 75.387 15562791 15.574 -0.319 152.561 0.069 Angola 2002 3.663 25.069 74.954 16109696 16.808 5.004 108.897 0.074 Angola 2003 3.882 28.120 69.570 16691395 6.626 98.224 0.071 Angola 2004 3.999 7.646 69.687 17295500 6.735 43.542 0.073 Angola 2005 4.075 -4.258 86.018 17912942 2.461 22.964 0.079 Angola 2006 4.848 -0.086 79.837 18541467 -5.009 13.303 0.088 Angola 2007 5.203 -1.446 76.397 19183907 1.874 12.249 0.101 Angola 2008 4.799 2.109 78.099 19842251 23.555 9.450 12.474 0.110 Angola 2009 6.075 3.384 55.055 20520103 24.992 29.185 13.731 0.114 Benin 1990 7.376 3.183 19.181 5001271 21.068 0.050 Benin 1991 7.160 6.080 21.002 5182525 13.947 0.050 Benin 1992 7.941 4.576 23.239 5378226 12.651 0.047 Benin 1993 7.426 0.062 22.464 5582420 8.642 0.441 0.047 Benin 1994 8.055 0.854 28.327 5786794 10.936 38.531 0.044 Benin 1995 8.077 0.614 27.369 5985658 10.869 14.463 0.043 Benin 1996 8.275 1.504 26.499 6176318 10.132 4.914 0.042 Benin 1997 8.469 1.191 27.176 6361301 7.042 3.466 0.041 Benin 1998 7.987 1.546 27.093 6546493 6.538 5.753 0.040 Benin 1999 8.140 1.578 29.062 6740491 20.794 6.033 0.327 0.039 Benin 2000 8.203 2.532 25.444 6949366 21.606 7.359 4.165 0.037 Benin 2001 8.520 1.755 22.348 7174911 23.376 4.061 3.984 0.038 Benin 2002 8.486 0.481 22.219 7414744 25.293 5.254 2.489 0.037 Benin 2003 8.279 1.257 20.976 7665681 26.581 8.695 1.487 0.036 Benin 2004 7.832 1.576 20.031 7922796 28.348 8.927 0.874 0.034 Benin 2005 7.843 -0.202 21.575 8182362 34.583 11.031 5.365 0.032 Benin 2006 7.537 -0.263 18.475 8443717 9.486 3.782 0.031 Benin 2007 7.518 2.525 19.544 8707637 8.108 1.298 0.030 75 University of Ghana http://ugspace.ug.edu.gh Benin 2008 7.174 0.724 19.782 8973525 13.846 7.947 0.031 Benin 2009 7.463 -0.284 15.784 9240982 17.884 2.157 0.031 Botswana 1990 5.116 2.530 55.055 1379814 39.767 -45.590 11.396 0.302 Botswana 1991 5.312 -0.208 53.030 1420098 48.065 -40.615 11.765 0.309 Botswana 1992 5.478 -0.038 48.180 1460453 47.446 -42.191 16.168 0.297 Botswana 1993 4.773 -6.898 47.095 1500356 51.415 -40.340 14.331 0.284 Botswana 1994 4.873 -0.326 49.278 1539135 50.218 -42.626 10.543 0.273 Botswana 1995 5.469 1.475 50.945 1576291 56.892 -35.847 10.513 0.268 Botswana 1996 5.417 1.483 54.191 1611827 -33.797 10.083 0.265 Botswana 1997 5.391 1.933 56.249 1645846 -73.660 8.720 0.271 Botswana 1998 5.270 1.836 48.881 1678111 72.561 -79.092 6.661 0.280 Botswana 1999 4.593 0.625 51.601 1708368 73.519 -63.383 7.749 0.275 Botswana 2000 4.542 1.015 53.265 1736579 75.214 -65.684 8.601 0.272 Botswana 2001 4.103 -1.156 44.270 1762531 74.758 -70.378 6.559 0.271 Botswana 2002 3.794 12.014 46.569 1786672 75.651 -28.734 8.033 0.284 Botswana 2003 4.106 9.528 45.353 1810438 76.293 -6.766 9.190 0.287 Botswana 2004 3.884 7.445 44.226 1835750 77.834 0.363 6.946 0.285 Botswana 2005 3.645 4.801 51.250 1864003 78.525 -5.472 8.610 0.271 Botswana 2006 3.320 6.670 47.014 1895671 80.533 -14.917 11.555 0.268 Botswana 2007 3.702 5.229 48.178 1930431 81.372 -18.441 7.081 0.267 Botswana 2008 3.512 6.713 42.121 1967866 -13.695 12.702 0.269 Botswana 2009 4.244 7.143 32.464 2007212 -1.114 8.027 0.260 Burkina Faso 1990 15.378 0.015 10.967 8811033 6.892 12.223 -0.504 0.029 Burkina Faso 1991 14.021 0.018 10.448 9050090 7.229 10.521 2.163 0.030 Burkina Faso 1992 15.159 0.139 8.846 9297116 7.478 8.820 -1.991 0.028 Burkina Faso 1993 14.764 0.136 8.952 9552473 7.935 8.175 0.553 0.028 Burkina Faso 1994 14.499 0.969 14.202 9816586 8.290 7.314 25.178 0.026 Burkina Faso 1995 15.234 0.413 14.141 10089876 5.581 7.459 0.026 Burkina Faso 1996 14.477 0.627 10.564 10372809 6.538 6.098 0.027 Burkina Faso 1997 15.647 0.399 10.813 10665781 13.108 2.319 0.026 Burkina Faso 1998 14.935 0.157 12.808 10969093 12.299 5.084 0.026 Burkina Faso 1999 16.853 0.263 9.518 11283016 9.793 11.538 -1.073 0.026 Burkina Faso 2000 16.332 0.889 9.077 11607944 10.422 14.326 -0.304 0.024 Burkina Faso 2001 13.135 0.326 9.243 11943740 10.639 13.467 5.007 0.025 Burkina Faso 2002 11.113 0.501 9.177 12290984 12.691 2.176 0.025 Burkina Faso 2003 14.262 0.733 8.710 12651596 12.077 13.629 2.035 0.025 Burkina Faso 2004 14.406 0.079 11.328 13028039 13.253 13.489 -0.400 0.024 Burkina Faso 2005 11.689 0.954 9.744 13421929 14.300 15.338 6.415 0.024 Burkina Faso 2006 11.341 1.433 11.349 13834195 15.041 15.280 2.333 0.024 Burkina Faso 2007 11.065 0.321 10.574 14264002 16.164 12.428 -0.231 0.023 Burkina Faso 2008 8.321 0.396 9.883 14709011 18.909 14.718 10.660 0.023 76 University of Ghana http://ugspace.ug.edu.gh Burkina Faso 2009 8.835 0.676 12.614 15165856 20.287 14.767 2.608 0.024 Burundi 1990 12.936 0.111 7.873 5613141 5.219 16.829 7.002 0.030 Burundi 1991 14.202 0.076 9.917 5759429 5.822 18.142 8.997 0.030 Burundi 1992 9.590 0.055 8.749 5895131 6.158 16.446 1.823 0.029 Burundi 1993 9.398 0.050 9.382 6019901 6.792 16.665 9.679 0.026 Burundi 1994 10.740 0.000 10.272 6134041 18.918 14.853 0.023 Burundi 1995 9.462 0.198 12.926 6239030 15.982 19.263 0.020 Burundi 1996 6.723 0.000 5.820 6333415 21.140 26.437 0.018 Burundi 1997 7.636 0.000 9.836 6420397 19.943 31.112 0.016 Burundi 1998 7.887 0.224 8.000 6511920 22.279 12.500 0.016 Burundi 1999 8.197 0.030 7.576 6623707 26.172 3.385 0.015 Burundi 2000 15.563 1.399 6.585 6767073 21.611 24.318 0.014 Burundi 2001 15.729 0.001 5.523 6946720 9.732 23.812 9.243 0.014 Burundi 2002 15.261 0.000 4.958 7159918 10.088 26.282 -1.371 0.014 Burundi 2003 14.537 0.001 6.596 7401215 10.242 26.320 10.762 0.013 Burundi 2004 13.784 0.005 7.080 7661613 11.655 25.627 7.852 0.012 Burundi 2005 12.945 0.052 8.202 7934213 12.699 23.360 13.524 0.011 Burundi 2006 13.099 0.003 7.532 8218070 13.895 28.581 2.809 0.011 Burundi 2007 12.816 0.038 7.033 8514578 14.828 23.819 8.342 0.011 Burundi 2008 11.384 0.237 6.513 8821795 19.768 24.107 0.011 Burundi 2009 11.079 0.019 5.399 9137786 19.760 23.274 10.981 0.011 Cameroon 1990 14.519 -1.012 20.183 12070359 25.452 30.864 1.099 0.090 Cameroon 1991 14.480 -0.117 19.999 12430311 26.923 37.547 0.060 0.083 Cameroon 1992 14.293 0.256 20.547 12796739 27.830 26.035 -0.016 0.075 Cameroon 1993 21.448 0.038 16.180 13169100 22.423 -3.207 0.068 Cameroon 1994 22.304 -0.098 21.121 13546823 26.705 19.922 35.094 0.062 Cameroon 1995 21.662 0.084 23.575 13929575 25.729 17.472 9.070 0.060 Cameroon 1996 20.063 1.041 23.381 14317191 15.619 3.924 0.059 Cameroon 1997 19.515 0.796 21.428 14709961 23.826 15.139 4.786 0.057 Cameroon 1998 20.721 2.234 21.430 15108630 24.568 15.198 3.171 0.056 Cameroon 1999 20.999 -0.148 21.502 15514249 25.942 15.550 1.872 0.055 Cameroon 2000 20.832 1.710 29.168 15927713 27.295 14.398 1.227 0.053 Cameroon 2001 20.939 0.761 27.496 16349364 32.046 15.248 4.420 0.053 Cameroon 2002 20.639 5.531 25.004 16779434 27.888 14.850 2.834 0.052 Cameroon 2003 20.250 2.469 23.957 17218591 29.410 15.359 0.623 0.051 Cameroon 2004 19.292 0.546 22.710 17667576 26.178 14.664 0.234 0.049 Cameroon 2005 17.706 1.468 24.465 18126999 26.644 13.774 2.014 0.047 Cameroon 2006 16.791 0.329 29.305 18597109 23.170 9.318 5.118 0.045 Cameroon 2007 16.718 0.927 31.020 19078100 31.764 6.972 0.921 0.044 Cameroon 2008 0.088 31.066 19570418 35.944 6.697 5.338 0.043 Cameroon 2009 3.336 23.518 20074522 39.636 7.846 3.044 0.045 77 University of Ghana http://ugspace.ug.edu.gh Cape Verde 1990 9.022 0.082 17.132 341256 20.921 41.582 48.052 0.062 Cape Verde 1991 10.581 0.545 16.564 349326 48.554 52.642 0.060 Cape Verde 1992 10.869 0.126 17.283 358473 53.248 54.282 0.058 Cape Verde 1993 7.133 0.743 13.261 368423 36.430 57.424 0.059 Cape Verde 1994 9.573 0.523 15.046 378763 29.737 53.014 59.407 0.058 Cape Verde 1995 10.096 5.374 17.137 389156 59.412 64.368 0.059 Cape Verde 1996 10.065 5.683 20.199 399508 57.727 68.207 0.059 Cape Verde 1997 9.728 2.360 27.442 409805 61.552 74.042 0.059 Cape Verde 1998 9.195 1.732 22.796 419884 58.504 77.296 0.060 Cape Verde 1999 8.758 9.000 20.976 429576 51.618 80.663 0.062 Cape Verde 2000 9.263 6.199 27.007 438737 67.298 64.685 78.665 0.062 Cape Verde 2001 7.197 1.619 29.809 447357 64.257 66.545 81.300 0.063 Cape Verde 2002 7.544 2.384 32.545 455396 66.594 71.822 82.832 0.064 Cape Verde 2003 7.005 4.823 31.364 462675 66.717 71.308 83.817 0.064 Cape Verde 2004 6.408 7.313 32.004 468985 65.706 72.215 82.232 0.062 Cape Verde 2005 6.453 8.277 37.777 474224 68.693 72.337 82.576 0.062 Cape Verde 2006 5.808 11.876 45.132 478265 80.028 74.943 87.010 0.064 Cape Verde 2007 14.413 42.825 481278 80.170 64.403 90.848 0.066 Cape Verde 2008 13.511 45.336 483824 83.100 67.841 97.010 0.069 Cape Verde 2009 7.940 35.609 486673 83.268 73.000 97.964 0.074 Central Africa 1990 11.653 0.047 14.764 2937832 11.420 13.169 -0.012 0.037 Central Africa 1991 12.151 -0.347 12.538 3010950 11.481 15.749 -2.762 0.035 Central Africa 1992 11.233 -0.744 11.503 3089141 9.578 13.517 -1.034 0.031 Central Africa 1993 11.230 -0.768 13.985 3170848 13.905 -2.915 0.029 Central Africa 1994 10.024 0.422 23.991 3253698 13.998 24.571 0.028 Central Africa 1995 10.402 0.553 20.388 3335840 11.376 19.189 0.029 Central Africa 1996 7.735 1.021 17.318 3417163 11.798 3.725 0.026 Central Africa 1997 6.087 0.150 19.516 3497910 10.849 1.611 0.025 Central Africa 1998 6.223 0.726 17.009 3577028 11.576 -1.885 0.025 Central Africa 1999 6.217 0.359 11.148 3653310 12.274 -1.414 0.024 Central Africa 2000 6.163 0.092 20.726 3726048 12.043 3.203 0.022 Central Africa 2001 6.000 0.556 17.156 3794677 11.810 14.332 3.835 0.021 Central Africa 2002 6.187 0.565 16.298 3859784 11.871 13.866 2.332 0.020 Central Africa 2003 6.086 1.948 13.501 3923294 15.003 4.135 0.018 Central Africa 2004 6.113 2.251 13.751 3987896 17.169 -2.066 0.017 Central Africa 2005 6.406 2.402 12.728 4055608 17.559 2.884 0.016 Central Africa 2006 6.538 2.351 14.192 4127112 18.238 6.695 0.015 Central Africa 2007 6.648 3.345 14.111 4202104 17.911 0.928 0.015 Central Africa 2008 6.705 5.878 11.008 4280405 18.185 9.273 0.015 Central Africa 2009 6.794 6.082 9.526 4361492 13.709 19.998 3.520 0.015 Chad 1990 14.406 0.541 13.480 5958022 6.638 11.465 -0.738 0.036 78 University of Ghana http://ugspace.ug.edu.gh Chad 1991 10.608 0.226 11.952 6151213 12.015 3.194 0.037 Chad 1992 10.583 0.104 11.091 6350174 14.121 -3.113 0.037 Chad 1993 11.155 1.036 13.323 6556628 14.635 -8.428 0.029 Chad 1994 9.219 2.295 16.106 6773104 7.224 12.033 41.725 0.030 Chad 1995 11.232 2.255 21.929 7001634 8.111 9.883 9.230 0.028 Chad 1996 10.522 2.455 17.544 7242018 8.464 11.078 11.331 0.027 Chad 1997 12.025 2.867 18.544 7494143 8.769 10.147 5.572 0.026 Chad 1998 11.279 1.244 18.492 7760157 9.574 9.101 4.259 0.026 Chad 1999 10.212 1.598 18.318 8042713 10.072 10.697 -8.025 0.024 Chad 2000 8.928 8.315 16.890 8343321 10.773 12.346 3.823 0.021 Chad 2001 9.558 26.903 14.666 8663599 12.508 13.049 12.431 0.023 Chad 2002 9.430 46.494 12.690 9002102 13.409 11.458 5.192 0.023 Chad 2003 7.983 26.041 24.633 9353516 14.792 11.453 -1.753 0.025 Chad 2004 5.146 10.573 51.009 9710498 15.195 7.907 -5.355 0.030 Chad 2005 5.343 -1.874 61.002 10067932 15.673 6.412 7.890 0.032 Chad 2006 5.611 -4.578 63.152 10423616 16.091 4.353 8.036 0.030 Chad 2007 6.178 -0.992 54.800 10779504 18.542 0.821 -8.975 0.028 Chad 2008 6.558 2.794 52.800 11139740 20.998 -1.270 10.297 0.027 Chad 2009 15.610 39.465 11510535 23.056 7.185 9.952 0.027 Comoros 1990 4.209 0.157 14.252 415144 20.569 0.052 Comoros 1991 4.178 1.015 20.011 427556 22.288 0.048 Comoros 1992 4.448 -0.544 18.041 440252 24.599 23.268 0.048 Comoros 1993 4.498 0.072 19.970 453188 18.634 0.047 Comoros 1994 5.965 0.096 20.073 466309 25.295 18.737 0.041 Comoros 1995 4.163 0.384 19.763 479574 16.876 0.040 Comoros 1996 4.163 0.221 18.324 492979 14.974 0.037 Comoros 1997 4.163 0.009 18.143 506525 15.613 0.036 Comoros 1998 4.164 0.178 11.354 520180 14.732 0.034 Comoros 1999 4.163 0.122 12.711 533909 32.796 15.017 0.032 Comoros 2000 4.533 0.046 16.737 547696 14.149 0.030 Comoros 2001 4.569 0.521 15.521 561525 9.841 5.555 0.029 Comoros 2002 4.365 0.171 15.731 575428 35.626 10.125 3.533 0.029 Comoros 2003 4.365 0.245 17.516 589500 39.427 10.788 3.799 0.028 Comoros 2004 4.417 0.185 15.114 603869 43.390 8.641 4.475 0.026 Comoros 2005 4.354 0.144 14.144 618632 11.386 3.013 0.025 Comoros 2006 4.187 0.205 14.197 633814 11.626 3.374 0.023 Comoros 2007 4.202 1.652 14.731 649404 12.177 4.466 0.022 Comoros 2008 4.246 0.874 13.910 665414 16.221 1.701 0.021 Comoros 2009 4.292 2.575 14.666 681845 20.390 4.362 0.022 Congo, Dem. Rep 1990 11.304 -0.155 29.504 34962676 81.295 0.027 Congo, Dem. Rep 1991 7.399 0.136 20.380 36309209 17.061 2154.437 0.024 79 University of Ghana http://ugspace.ug.edu.gh Congo, Dem. Rep 1992 4.962 -0.009 16.678 37783835 22.832 14.274 4129.170 0.020 Congo, Dem. Rep 1993 6.894 0.064 11.328 39314955 24.389 16.400 1986.905 0.016 Congo, Dem. Rep 1994 -0.026 22.625 40804011 25.766 3.429 23773.132 0.014 Congo, Dem. Rep 1995 -0.396 28.482 42183620 27.929 1.698 541.909 0.013 Congo, Dem. Rep 1996 8.916 0.430 30.003 43424997 492.442 0.012 Congo, Dem. Rep 1997 6.334 -0.728 18.750 44558347 198.517 0.010 Congo, Dem. Rep 1998 6.069 0.987 29.790 45647949 29.149 0.010 Congo, Dem. Rep 1999 5.046 0.237 23.636 46788238 284.895 0.009 Congo, Dem. Rep 2000 4.821 1.672 22.392 48048664 1.268 513.907 0.007 Congo, Dem. Rep 2001 4.865 1.711 18.635 49449015 1.184 359.937 0.007 Congo, Dem. Rep 2002 5.421 2.545 21.103 50971407 0.210 31.523 0.007 Congo, Dem. Rep 2003 5.421 6.901 26.642 52602208 1.161 12.874 0.007 Congo, Dem. Rep 2004 5.387 6.282 30.675 54314855 0.824 3.994 0.007 Congo, Dem. Rep 2005 5.400 2.317 33.618 56089536 1.729 21.317 0.007 Congo, Dem. Rep 2006 5.242 2.694 34.205 57926840 2.896 13.053 0.006 Congo, Dem. Rep 2007 5.120 17.912 65.164 59834875 35.066 3.642 16.945 0.006 Congo, Dem. Rep 2008 4.958 14.327 61.301 61809278 37.747 6.064 17.301 0.006 Congo, Dem. Rep 2009 4.973 -2.481 45.205 63845097 39.673 5.223 2.800 0.007 Congo, Rep. 1990 8.346 0.806 51.772 2386467 48.399 29.142 2.889 0.154 Congo, Rep. 1991 8.937 1.202 41.635 2450125 47.092 29.870 -1.678 0.151 Congo, Rep. 1992 7.895 0.093 40.692 2514907 47.801 30.618 -3.935 0.145 Congo, Rep. 1993 7.986 14.907 42.836 2581306 21.135 4.924 0.135 Congo, Rep. 1994 7.757 0.169 58.517 2649964 50.281 16.896 42.440 0.118 Congo, Rep. 1995 8.133 5.908 64.705 2721277 17.330 9.418 0.115 Congo, Rep. 1996 6.702 2.857 68.458 2795903 48.314 15.264 10.031 0.112 Congo, Rep. 1997 5.458 3.409 75.597 2873638 16.729 0.103 Congo, Rep. 1998 6.956 1.681 76.278 2953011 21.974 0.099 Congo, Rep. 1999 5.465 22.865 72.290 3031969 19.041 4.143 0.089 Congo, Rep. 2000 3.477 5.152 80.297 3109269 8.651 -0.882 0.089 Congo, Rep. 2001 4.511 2.763 77.420 3183883 13.343 0.056 0.088 Congo, Rep. 2002 5.321 10.965 81.516 3256867 39.064 11.835 4.379 0.088 Congo, Rep. 2003 6.024 9.243 80.812 3331564 40.461 12.966 -0.632 0.083 Congo, Rep. 2004 4.980 -0.183 80.530 3412592 45.893 11.342 2.430 0.080 Congo, Rep. 2005 3.999 13.159 84.158 3503086 1.104 3.094 0.080 Congo, Rep. 2006 3.574 19.243 84.160 3604595 -8.279 6.538 0.078 Congo, Rep. 2007 4.032 31.429 78.529 3715665 -6.851 2.656 0.072 Congo, Rep. 2008 3.465 21.297 75.151 3832771 -15.246 7.334 0.074 Congo, Rep. 2009 4.471 19.404 70.418 3950786 -14.361 5.299 0.079 Cote d'Ivoire 1990 20.904 0.446 31.690 12165909 44.470 -0.806 0.083 Cote d'Ivoire 1991 19.429 0.155 30.012 12600967 44.028 1.683 0.079 Cote d'Ivoire 1992 19.071 -2.070 31.909 13046907 45.209 4.231 0.073 80 University of Ghana http://ugspace.ug.edu.gh Cote d'Ivoire 1993 15.432 0.796 29.442 13499696 41.221 2.165 0.068 Cote d'Ivoire 1994 14.742 0.938 40.527 13953779 29.732 26.082 0.063 Cote d'Ivoire 1995 15.049 1.923 41.759 14404340 28.048 14.295 0.064 Cote d'Ivoire 1996 18.209 2.217 41.101 14852193 25.654 2.481 0.064 Cote d'Ivoire 1997 21.375 3.543 41.420 15296390 25.182 4.021 0.062 Cote d'Ivoire 1998 19.546 2.973 39.413 15728482 24.347 4.611 0.061 Cote d'Ivoire 1999 20.489 2.578 40.351 16137824 24.289 23.488 0.702 0.058 Cote d'Ivoire 2000 21.678 2.253 40.428 16517948 22.404 2.531 0.052 Cote d'Ivoire 2001 21.032 2.586 41.843 16865376 20.362 4.362 0.049 Cote d'Ivoire 2002 19.495 1.851 50.028 17185421 19.624 3.077 0.047 Cote d'Ivoire 2003 17.848 1.204 45.837 17491539 16.876 3.297 0.044 Cote d'Ivoire 2004 18.605 1.828 48.556 17802516 17.354 1.458 0.042 Cote d'Ivoire 2005 19.289 2.132 51.052 18132702 17.407 3.886 0.039 Cote d'Ivoire 2006 17.839 2.019 52.651 18486392 17.580 2.467 0.037 Cote d'Ivoire 2007 17.535 2.239 47.816 18862172 20.128 1.892 0.036 Cote d'Ivoire 2008 18.021 1.992 46.512 19261647 19.392 6.309 0.035 Cote d'Ivoire 2009 18.170 1.719 42.195 19684909 21.864 1.020 0.037 Djibouti 1990 3.646 53.838 562290 10.771 45.444 0.119 Djibouti 1991 3.640 0.495 53.838 581641 10.670 43.138 0.108 Djibouti 1992 3.394 0.479 44.550 594437 10.927 41.178 0.101 Djibouti 1993 3.349 0.305 43.983 603534 10.417 39.607 0.090 Djibouti 1994 2.810 0.290 41.929 613219 10.938 38.309 0.084 Djibouti 1995 2.799 0.647 39.145 626537 11.773 42.847 0.076 Djibouti 1996 2.889 0.661 38.518 644434 11.961 44.348 0.068 Djibouti 1997 2.885 0.617 38.825 665755 12.994 43.176 0.062 Djibouti 1998 2.704 0.617 40.446 688940 46.061 0.057 Djibouti 1999 2.579 0.599 37.316 711573 28.855 0.054 Djibouti 2000 2.611 0.596 35.056 731930 13.649 32.065 0.050 Djibouti 2001 2.610 0.593 37.296 749604 15.652 26.440 1.747 0.049 Djibouti 2002 2.644 0.581 38.571 765283 16.400 24.396 0.638 0.048 Djibouti 2003 2.670 2.287 39.912 779640 18.450 22.584 1.982 0.047 Djibouti 2004 2.661 5.787 36.971 793738 20.374 21.112 3.122 0.045 Djibouti 2005 2.586 3.132 40.611 808367 22.643 20.069 3.105 0.043 Djibouti 2006 2.517 14.084 39.887 823682 22.285 20.183 3.483 0.042 Djibouti 2007 2.450 23.039 57.088 839453 25.379 22.522 4.966 0.042 Djibouti 2008 23.170 855636 29.244 24.746 11.959 0.043 Djibouti 2009 9.233 872090 31.460 29.349 1.675 0.046 Eqn Guinea 1990 8.381 32.152 377363 36.805 0.858 0.087 Eqn Guinea 1991 1.722 31.643 48.234 390381 68.670 -3.425 0.082 Eqn Guinea 1992 1.684 3.903 1.946 404081 53.088 -4.279 0.084 Eqn Guinea 1993 1.597 14.660 1.972 418409 41.832 36.237 5.452 0.083 81 University of Ghana http://ugspace.ug.edu.gh Eqn Guinea 1994 13.516 56.022 433197 37.696 36.671 31.841 0.080 Eqn Guinea 1995 77.402 55.204 448332 29.417 19.872 0.086 Eqn Guinea 1996 145.202 77.578 463844 19.386 4.541 0.102 Eqn Guinea 1997 10.739 100.822 479836 10.349 3.017 0.161 Eqn Guinea 1998 60.283 101.748 496330 10.363 7.936 0.182 Eqn Guinea 1999 17.694 513347 32.279 8.035 0.372 0.237 Eqn Guinea 2000 1.435 12.431 99.456 530896 30.569 4.670 4.802 0.245 Eqn Guinea 2001 6.184 54.187 101.354 549007 26.959 0.890 8.825 0.379 Eqn Guinea 2002 6.431 15.064 99.619 567664 26.719 0.289 7.592 0.428 Eqn Guinea 2003 6.726 23.364 96.849 586772 1.272 7.324 0.455 Eqn Guinea 2004 5.732 6.505 90.131 606201 -11.477 4.220 0.578 Eqn Guinea 2005 6.153 9.360 87.417 625866 27.371 -21.513 5.632 0.584 Eqn Guinea 2006 7.269 4.889 86.758 645718 -25.589 4.416 0.547 Eqn Guinea 2007 11.739 9.882 81.892 665798 -25.932 2.804 0.621 Eqn Guinea 2008 12.613 -4.309 78.807 686223 -23.496 6.552 0.663 Eqn Guinea 2009 13.387 69.798 707155 -14.305 4.691 0.696 Eritrea 1990 3139083 Eritrea 1991 3160644 Eritrea 1992 8.246 11.424 3160617 0.018 Eritrea 1993 9.028 30.557 3150811 13.397 0.020 Eritrea 1994 7.745 28.020 3147871 14.325 0.023 Eritrea 1995 9.042 22.388 3164095 15.215 50.097 0.023 Eritrea 1996 9.558 5.292 29.234 3202598 15.985 77.291 0.024 Eritrea 1997 10.701 5.987 29.615 3260612 17.103 73.332 0.024 Eritrea 1998 9.382 19.921 14.834 3337227 111.503 0.022 Eritrea 1999 10.043 12.080 9.534 3429656 20.462 147.849 0.021 Eritrea 2000 11.159 4.400 15.119 3535156 23.021 150.463 0.016 Eritrea 2001 10.543 1.760 11.825 3655006 23.279 133.079 0.019 Eritrea 2002 10.989 2.961 12.720 3788532 24.262 133.308 0.018 Eritrea 2003 9.903 2.528 6.442 3928408 25.050 130.744 0.016 Eritrea 2004 9.333 -0.710 5.775 4064958 25.697 120.336 0.015 Eritrea 2005 7.314 -0.095 6.166 4191273 28.468 125.727 0.014 Eritrea 2006 6.352 0.037 6.899 4304440 29.920 124.450 0.013 Eritrea 2007 5.715 -0.008 6.530 4406299 29.029 121.143 0.012 Eritrea 2008 6.760 -0.017 4.434 4500638 30.874 135.100 0.010 Eritrea 2009 5.650 0.002 4.529 4593549 32.859 113.125 0.011 Ethiopia 1990 4.786 5.560 48057094 39.164 5.152 0.024 Ethiopia 1991 3.034 4.064 49784987 13.890 38.707 35.723 0.021 Ethiopia 1992 2.813 0.002 3.212 51602776 12.158 39.334 10.527 0.018 Ethiopia 1993 3.934 0.047 5.782 53477944 10.833 32.497 3.543 0.019 Ethiopia 1994 4.450 0.237 7.122 55366517 10.468 33.209 7.594 0.018 82 University of Ghana http://ugspace.ug.edu.gh Ethiopia 1995 4.796 0.183 9.689 57237226 10.502 30.141 10.022 0.018 Ethiopia 1996 5.130 0.260 9.264 59076414 10.860 31.971 -8.484 0.018 Ethiopia 1997 5.044 3.350 11.365 60893264 35.905 2.395 0.018 Ethiopia 1998 4.936 3.340 12.869 62707547 41.301 0.895 0.016 Ethiopia 1999 5.482 0.945 11.727 64550161 12.389 47.923 7.941 0.015 Ethiopia 2000 5.515 1.660 12.033 66443603 13.502 48.642 0.662 0.015 Ethiopia 2001 5.721 4.344 11.999 68393128 16.389 44.198 -8.238 0.015 Ethiopia 2002 5.678 3.283 12.604 70391170 18.608 46.331 1.654 0.015 Ethiopia 2003 5.670 5.446 13.317 72432290 19.499 46.823 17.762 0.014 Ethiopia 2004 5.324 5.432 14.902 74506974 21.911 45.943 3.256 0.014 Ethiopia 2005 4.778 2.158 15.100 76608431 24.750 49.451 12.945 0.015 Ethiopia 2006 4.538 3.603 13.882 78735675 28.839 46.828 12.310 0.015 Ethiopia 2007 5.017 1.157 12.728 80891968 31.990 41.842 17.238 0.016 Ethiopia 2008 4.765 0.420 11.404 83079608 33.204 36.863 44.391 0.017 Ethiopia 2009 3.991 0.778 10.578 85302099 33.489 8.468 0.019 Gabon 1990 5.584 1.234 46.039 952269 19.974 7.725 0.649 Gabon 1991 6.172 -1.010 47.263 978252 21.862 -11.686 0.655 Gabon 1992 6.195 2.270 46.076 1004598 21.012 -9.543 0.590 Gabon 1993 6.292 -2.597 48.795 1031358 20.505 0.534 0.572 Gabon 1994 4.731 -2.377 61.676 1058625 38.967 16.906 36.116 0.549 Gabon 1995 4.525 -6.343 59.387 1086449 41.593 18.331 9.647 0.539 Gabon 1996 4.061 -8.589 62.663 1114879 49.221 15.007 0.690 0.520 Gabon 1997 4.397 -5.844 61.304 1143838 49.698 16.083 3.973 0.509 Gabon 1998 5.611 3.270 47.406 1173114 23.795 1.449 0.491 Gabon 1999 4.727 -3.359 59.613 1202412 47.873 20.991 -1.937 0.415 Gabon 2000 3.725 -0.841 69.032 1231548 12.519 0.505 0.378 Gabon 2001 4.556 -1.890 59.032 1260435 52.477 20.682 2.138 0.369 Gabon 2002 4.841 0.790 53.564 1289192 53.304 19.824 0.037 0.351 Gabon 2003 4.720 2.609 55.335 1318093 18.466 2.235 0.339 Gabon 2004 4.554 4.451 62.204 1347524 12.909 0.408 0.320 Gabon 2005 4.115 3.764 64.738 1377777 9.773 3.708 0.306 Gabon 2006 4.099 2.805 61.931 1408920 8.764 -1.409 0.290 Gabon 2007 4.060 2.328 62.248 1440902 2.316 5.030 0.289 Gabon 2008 3.462 1.438 66.567 1473741 6.061 5.264 0.287 Gabon 2009 4.128 0.300 56.144 1507428 7.896 1.886 0.287 Gambia, The 1990 5.530 4.453 59.903 916811 17.186 3.408 12.168 0.066 Gambia, The 1991 9.213 1.332 29.439 949490 17.958 -0.002 8.642 0.065 Gambia, The 1992 8.896 0.886 30.593 979701 18.301 4.379 9.487 0.062 Gambia, The 1993 8.704 1.466 28.192 1008296 20.673 3.344 6.464 0.060 Gambia, The 1994 8.025 1.302 21.240 1036627 20.549 2.787 1.710 0.056 Gambia, The 1995 8.153 0.983 23.776 1065746 22.814 3.747 6.981 0.053 83 University of Ghana http://ugspace.ug.edu.gh Gambia, The 1996 7.574 1.257 21.799 1095839 22.196 3.681 1.099 0.050 Gambia, The 1997 8.020 1.452 22.932 1126786 5.045 2.781 0.049 Gambia, The 1998 7.544 25.306 1159001 5.423 1.114 0.047 Gambia, The 1999 7.154 24.397 1192920 6.865 3.812 0.046 Gambia, The 2000 6.767 25.799 1228863 7.405 0.845 0.045 Gambia, The 2001 6.632 21.821 1267103 14.490 4.493 0.045 Gambia, The 2002 7.021 27.157 1307674 17.404 8.609 0.041 Gambia, The 2003 5.917 3.752 31.094 1350345 25.212 17.033 0.041 Gambia, The 2004 5.600 9.594 34.225 1394727 17.309 14.207 0.040 Gambia, The 2005 6.645 8.595 32.757 1440542 19.573 4.839 0.037 Gambia, The 2006 6.848 12.550 33.847 1487731 23.394 2.057 0.035 Gambia, The 2007 6.677 9.776 28.891 1536424 20.845 5.369 0.034 Gambia, The 2008 5.790 8.140 23.453 1586749 57.841 28.110 4.444 0.034 Gambia, The 2009 4.980 4.380 25.350 1638899 58.835 31.173 4.562 0.037 Ghana 1990 9.811 0.251 16.878 14628260 35.947 17.508 37.259 0.039 Ghana 1991 9.282 0.303 16.964 15042736 16.383 18.031 0.040 Ghana 1992 9.370 0.351 17.226 15471527 20.571 10.056 0.038 Ghana 1993 10.509 2.094 20.254 15907244 21.348 24.960 0.038 Ghana 1994 10.116 4.278 25.259 16339344 18.399 24.870 0.036 Ghana 1995 10.271 1.647 24.496 16760991 35.840 18.887 59.462 0.035 Ghana 1996 9.725 1.731 32.112 17169214 18.364 46.561 0.035 Ghana 1997 10.132 1.187 32.410 17568583 25.511 27.885 0.033 Ghana 1998 10.040 2.237 33.871 17969006 24.568 14.624 0.033 Ghana 1999 10.072 3.158 32.078 18384426 40.739 32.523 12.409 0.032 Ghana 2000 10.076 3.329 48.802 18824994 41.037 39.298 25.193 0.030 Ghana 2001 10.046 1.681 45.233 19293804 39.032 35.130 32.905 0.030 Ghana 2002 10.071 0.956 42.616 19788181 41.024 34.592 14.816 0.030 Ghana 2003 9.883 1.792 40.679 20305396 42.389 26.922 26.675 0.030 Ghana 2004 9.570 1.568 39.303 20840493 45.166 31.426 12.625 0.029 Ghana 2005 9.460 1.351 36.449 21389514 47.340 30.840 15.118 0.028 Ghana 2006 10.239 3.116 25.193 21951891 49.048 21.104 10.915 0.028 Ghana 2007 9.150 5.587 24.525 22528041 53.431 22.863 10.733 0.028 Ghana 2008 7.943 9.517 25.029 23115919 55.780 27.900 16.522 0.030 Ghana 2009 6.949 9.133 29.291 23713164 58.294 28.700 19.251 0.031 Guinea 1990 4.555 0.670 31.075 6034082 11.105 0.039 Guinea 1991 4.039 1.286 30.331 6367110 11.522 6.012 0.037 Guinea 1992 4.640 0.599 24.875 6751394 5.739 0.035 Guinea 1993 4.762 0.083 26.518 7155564 12.668 5.909 0.033 Guinea 1994 4.669 0.006 22.752 7536389 12.956 6.610 0.031 Guinea 1995 3.982 0.021 21.119 7863033 7.711 0.030 Guinea 1996 3.825 0.614 19.195 8124799 13.255 8.513 0.029 84 University of Ghana http://ugspace.ug.edu.gh Guinea 1997 3.624 0.457 19.561 8331366 7.426 0.028 Guinea 1998 3.833 0.496 21.287 8497582 6.479 0.028 Guinea 1999 4.018 1.833 21.640 8647336 13.347 0.027 Guinea 2000 4.026 0.332 24.524 8799165 8.913 0.026 Guinea 2001 4.035 0.059 28.579 8955756 17.859 9.543 0.026 Guinea 2002 4.099 1.017 26.478 9114287 20.533 13.628 0.026 Guinea 2003 6.370 2.291 25.957 9281572 22.259 16.060 0.025 Guinea 2004 6.088 2.670 24.626 9464771 24.537 16.321 0.024 Guinea 2005 6.597 34.786 9669023 29.194 17.744 31.373 0.023 Guinea 2006 6.156 40.590 9898301 32.694 22.070 34.695 0.022 Guinea 2007 7.019 28.765 10152521 17.902 22.844 0.021 Guinea 2008 6.828 0.282 34.933 10427356 34.082 19.017 18.384 0.021 Guinea 2009 7.377 2.186 26.538 10715770 21.156 4.684 0.022 Guinea-Bissau 1990 8.398 0.828 9.936 1056208 77.475 33.002 0.052 Guinea-Bissau 1991 3.814 0.813 9.982 1080191 13.372 57.595 0.053 Guinea-Bissau 1992 2.499 2.576 4.902 1104708 10.413 69.584 0.050 Guinea-Bissau 1993 3.544 1.393 8.869 1129706 10.411 48.108 0.049 Guinea-Bissau 1994 7.229 0.182 16.454 1155111 9.360 15.176 0.047 Guinea-Bissau 1995 7.895 0.016 11.667 1180877 3.074 45.365 0.046 Guinea-Bissau 1996 7.407 0.381 10.524 1207006 3.117 50.734 0.049 Guinea-Bissau 1997 11.201 4.274 21.017 1233520 7.294 49.101 0.048 Guinea-Bissau 1998 9.287 2.135 14.444 1260424 10.815 8.014 0.032 Guinea-Bissau 1999 10.726 0.326 24.861 1287727 13.716 -2.086 0.031 Guinea-Bissau 2000 10.501 0.326 31.775 1315455 17.188 10.551 8.636 0.030 Guinea-Bissau 2001 10.164 0.199 28.606 1343646 6.899 3.348 0.030 Guinea-Bissau 2002 10.605 1.749 29.819 1372367 7.945 3.300 0.030 Guinea-Bissau 2003 0.843 1401716 6.421 -3.503 0.027 Guinea-Bissau 2004 0.330 1431816 4.269 0.883 0.025 Guinea-Bissau 2005 1.517 1462784 30.533 5.118 3.329 0.024 Guinea-Bissau 2006 3.092 1494603 32.641 5.407 1.955 0.022 Guinea-Bissau 2007 2.717 1527342 6.207 4.617 0.022 Guinea-Bissau 2008 0.786 1561293 7.171 10.460 0.023 Guinea-Bissau 2009 -0.162 1596832 4.970 -1.651 0.024 Kenya 1990 11.723 0.666 25.693 23446229 35.817 17.782 0.062 Kenya 1991 12.048 0.231 27.042 24234087 37.382 20.084 0.059 Kenya 1992 10.793 0.078 26.260 25029754 37.264 27.332 0.055 Kenya 1993 10.008 2.532 38.904 25824736 29.056 45.979 0.051 Kenya 1994 10.695 0.104 37.040 26608089 36.226 28.814 0.049 Kenya 1995 9.882 0.467 32.592 27373035 42.746 1.554 0.048 Kenya 1996 13.260 0.902 25.201 28116027 34.308 8.864 0.046 Kenya 1997 12.910 0.473 22.686 28842245 37.104 11.362 0.043 85 University of Ghana http://ugspace.ug.edu.gh Kenya 1998 12.303 0.188 20.169 29564614 36.631 6.722 0.041 Kenya 1999 11.429 0.403 20.833 30301240 38.521 37.654 5.742 0.039 Kenya 2000 11.624 0.873 21.588 31065820 39.312 35.746 9.980 0.037 Kenya 2001 11.002 0.041 22.932 31863280 40.356 36.414 5.739 0.036 Kenya 2002 11.069 0.210 24.898 32691980 40.995 38.979 1.961 0.035 Kenya 2003 10.922 0.548 24.087 33551079 43.138 38.974 9.816 0.033 Kenya 2004 11.250 0.286 26.610 34437460 47.189 39.380 11.624 0.032 Kenya 2005 11.824 0.113 28.509 35349040 47.899 37.361 10.313 0.032 Kenya 2006 11.549 0.225 27.112 36286015 50.099 32.003 14.454 0.031 Kenya 2007 11.788 2.677 26.779 37250540 52.673 31.093 9.759 0.031 Kenya 2008 12.286 0.314 27.607 38244442 59.406 33.903 26.240 0.031 Kenya 2009 11.277 0.380 24.152 39269988 60.432 35.577 9.234 0.032 Lesotho 1990 14.500 3.136 17.958 1597534 25.291 33.482 11.635 0.039 Lesotho 1991 14.643 1.227 18.713 1627900 24.843 26.909 17.678 0.039 Lesotho 1992 16.158 0.370 20.244 1660360 26.347 12.354 17.209 0.040 Lesotho 1993 15.980 2.076 23.619 1693459 27.465 1.712 13.136 0.039 Lesotho 1994 16.802 2.481 24.478 1725118 30.006 -4.236 8.215 0.039 Lesotho 1995 16.308 32.050 22.452 1753824 31.659 -9.016 9.271 0.037 Lesotho 1996 17.631 35.235 29.892 1779201 31.431 -14.332 9.330 0.037 Lesotho 1997 18.622 31.186 28.952 1801695 32.699 -19.067 0.036 Lesotho 1998 18.324 32.315 32.208 1821632 32.267 -20.874 0.034 Lesotho 1999 17.853 20.378 28.364 1839631 32.492 -1.129 0.032 Lesotho 2000 13.548 4.196 34.847 1856225 32.448 3.617 6.132 0.031 Lesotho 2001 18.870 4.198 53.849 1871489 34.315 5.019 -9.616 0.032 Lesotho 2002 22.535 4.316 66.169 1885488 35.322 10.793 33.813 0.031 Lesotho 2003 21.209 4.528 60.069 1898778 35.883 4.644 6.629 0.031 Lesotho 2004 21.955 4.505 56.369 1912042 37.948 -1.842 5.023 0.029 Lesotho 2005 19.377 5.138 48.873 1925844 39.588 -1.155 3.438 0.028 Lesotho 2006 20.997 4.189 53.544 1940345 39.840 -5.962 6.073 0.028 Lesotho 2007 18.982 6.667 52.103 1955656 41.839 -18.401 8.012 0.028 Lesotho 2008 18.929 6.885 56.279 1972194 43.799 -18.304 10.716 0.029 Lesotho 2009 15.892 5.839 45.816 1990413 46.872 -14.730 7.379 0.031 Liberia 1990 58.595 2102877 0.015 Liberia 1991 2.417 2066060 3170.321 0.013 Liberia 1992 -4.855 2028672 0.008 Liberia 1993 -33.429 2006349 0.005 Liberia 1994 13.147 2019148 0.004 Liberia 1995 2.745 3.412 2079921 0.004 Liberia 1996 -82.892 2197801 0.004 Liberia 1997 5.509 72.261 8.787 2365290 0.007 Liberia 1998 4.811 52.923 10.790 2558085 247.465 0.008 86 University of Ghana http://ugspace.ug.edu.gh Liberia 1999 4.844 58.004 14.554 2741755 31.498 182.060 0.009 Liberia 2000 4.052 3.931 26.139 2891968 35.205 174.465 0.010 Liberia 2001 4.180 1.612 28.561 2998770 170.851 0.011 Liberia 2002 3.239 0.522 35.714 3070673 183.869 14.160 0.014 Liberia 2003 4.125 91.007 91.514 3124222 210.801 10.330 0.009 Liberia 2004 7.730 16.135 26.459 3184643 227.784 7.829 0.008 Liberia 2005 7.227 15.277 23.817 3269786 188.370 10.834 0.008 Liberia 2006 7.584 17.857 30.807 3384804 185.778 7.341 0.008 Liberia 2007 7.339 17.813 32.372 3522337 159.718 11.391 0.009 Liberia 2008 6.004 33.330 34.366 3672782 143.225 17.490 0.009 Liberia 2009 4.298 11.064 15.264 3821498 113.387 7.428 0.010 Madagascar 1990 11.155 0.727 16.602 11545782 18.268 22.151 11.784 0.045 Madagascar 1991 11.005 0.516 18.110 11898267 23.948 8.593 0.040 Madagascar 1992 10.425 0.699 16.384 12263899 28.299 14.512 0.038 Madagascar 1993 9.859 0.456 15.318 12643864 29.074 10.008 0.036 Madagascar 1994 8.023 0.192 22.032 13039754 25.761 38.942 0.033 Madagascar 1995 7.944 0.307 24.134 13452526 17.347 49.080 0.032 Madagascar 1996 9.660 0.254 20.492 13882646 14.223 19.756 0.030 Madagascar 1997 11.290 0.395 21.866 14329239 13.250 4.486 0.029 Madagascar 1998 11.612 0.445 21.521 14790245 15.499 6.208 0.028 Madagascar 1999 11.732 1.571 24.465 15262817 15.043 9.930 0.027 Madagascar 2000 12.239 2.139 30.679 15744811 15.157 11.860 0.026 Madagascar 2001 12.433 2.054 29.079 16235767 16.168 6.938 0.026 Madagascar 2002 12.502 0.333 16.008 16736029 18.326 15.932 0.021 Madagascar 2003 13.713 0.235 23.086 17245275 17.911 -1.225 0.022 Madagascar 2004 14.188 1.212 32.636 17763367 15.012 13.811 0.021 Madagascar 2005 13.991 1.696 28.216 18290394 21.148 12.836 18.513 0.020 Madagascar 2006 14.342 5.340 29.732 18826129 23.892 9.633 10.772 0.020 Madagascar 2007 14.466 10.531 30.324 19371031 26.478 9.966 10.301 0.020 Madagascar 2008 14.336 12.447 26.593 19926798 29.042 9.376 9.224 0.020 Madagascar 2009 14.140 12.560 28.826 20495706 30.385 11.667 8.957 0.019 Malawi 1990 19.469 1.239 23.782 8321318 16.095 19.679 11.824 0.025 Malawi 1991 17.969 -1.302 23.272 8462740 16.532 20.165 12.615 0.026 Malawi 1992 21.167 -0.395 23.206 8499859 19.015 31.189 23.751 0.023 Malawi 1993 15.662 0.386 16.130 8479806 18.068 25.632 22.773 0.024 Malawi 1994 17.394 2.115 29.633 8470327 16.094 29.981 34.650 0.020 Malawi 1995 15.822 0.404 30.368 8520012 23.607 14.647 83.326 0.023 Malawi 1996 14.300 0.693 22.826 8642945 24.655 10.486 37.602 0.023 Malawi 1997 13.546 0.558 21.357 8823757 9.964 9.137 0.022 Malawi 1998 13.610 0.691 32.770 9044092 31.861 8.945 29.749 0.021 Malawi 1999 13.384 3.296 28.022 9302180 36.017 10.684 44.804 0.020 87 University of Ghana http://ugspace.ug.edu.gh Malawi 2000 12.881 1.491 25.604 9557899 30.216 12.490 29.581 0.019 Malawi 2001 11.535 1.124 27.991 9802756 30.983 15.792 22.700 0.017 Malawi 2002 10.594 0.221 20.803 10045649 29.897 11.752 14.745 0.017 Malawi 2003 11.856 3.429 26.697 10292018 12.097 9.577 0.017 Malawi 2004 9.997 4.941 24.960 10550942 27.450 12.115 11.430 0.016 Malawi 2005 9.178 5.071 24.047 10828785 27.285 12.534 15.410 0.015 Malawi 2006 10.713 1.141 22.620 11126644 29.147 11.023 13.974 0.014 Malawi 2007 11.393 3.410 28.329 11441899 28.872 12.955 7.952 0.015 Malawi 2008 12.670 4.569 28.189 11773641 31.214 21.011 8.713 0.015 Malawi 2009 12.308 0.977 24.649 12118513 31.839 24.463 8.422 0.017 Mali 1990 8.547 0.237 17.149 8482075 6.775 12.346 0.606 0.029 Mali 1991 8.519 0.050 18.236 8672581 7.202 11.390 1.800 0.028 Mali 1992 7.497 -0.767 15.434 8891141 8.232 11.932 -6.243 0.029 Mali 1993 7.554 0.152 15.838 9131449 8.335 12.494 -0.264 0.026 Mali 1994 7.720 0.988 23.006 9383608 9.518 10.338 23.177 0.025 Mali 1995 7.977 4.518 21.112 9640643 10.665 10.133 13.442 0.025 Mali 1996 7.643 1.710 20.034 9901045 11.198 9.672 6.805 0.024 Mali 1997 4.151 2.545 26.118 10168000 12.191 11.308 -0.363 0.023 Mali 1998 4.327 0.342 24.786 10444822 13.258 13.281 4.037 0.023 Mali 1999 4.063 0.085 26.451 10736542 14.977 12.726 -1.202 0.023 Mali 2000 3.811 3.403 26.782 11046926 17.297 11.746 -0.678 0.022 Mali 2001 3.086 4.629 33.297 11376094 12.968 5.187 0.023 Mali 2002 3.169 7.293 31.875 11723017 14.165 5.033 0.023 Mali 2003 2.832 3.032 26.421 12088867 21.910 14.919 -1.347 0.023 Mali 2004 3.372 2.072 25.375 12474857 23.749 15.849 -3.100 0.021 Mali 2005 3.191 3.296 25.622 12881384 25.553 13.777 6.398 0.021 Mali 2006 3.081 0.289 32.113 13309942 27.387 12.391 1.544 0.020 Mali 2007 3.106 -0.701 26.175 13759226 29.936 12.674 1.412 0.020 Mali 2008 1.148 29.200 14223403 33.306 11.822 9.171 0.020 Mali 2009 1.146 23.742 14694565 36.199 9.423 2.464 0.021 Mauritania 1990 10.280 0.661 45.642 2023665 13.422 47.027 6.600 0.077 Mauritania 1991 7.586 0.157 34.743 2080782 13.147 36.116 5.629 0.074 Mauritania 1992 8.486 0.516 31.494 2140250 13.553 10.142 0.071 Mauritania 1993 9.786 1.287 34.663 2202201 14.267 9.370 0.070 Mauritania 1994 8.661 0.158 48.540 2266745 14.784 4.128 0.063 Mauritania 1995 8.301 0.494 58.027 2333966 15.390 6.544 0.065 Mauritania 1996 8.521 -0.030 55.782 2403779 15.817 4.681 0.064 Mauritania 1997 9.133 -0.238 44.835 2476188 15.641 4.625 0.057 Mauritania 1998 14.617 0.010 30.856 2551429 16.457 8.032 0.055 Mauritania 1999 15.213 1.076 27.239 2629806 17.443 4.074 0.055 Mauritania 2000 13.197 3.099 29.961 2711421 18.038 3.254 0.050 88 University of Ghana http://ugspace.ug.edu.gh Mauritania 2001 13.640 5.920 29.398 2796502 20.487 4.715 0.049 Mauritania 2002 13.750 5.089 28.338 2884672 20.464 3.896 0.047 Mauritania 2003 11.865 6.519 21.450 2974686 21.343 5.152 0.046 Mauritania 2004 11.633 21.359 25.649 3064882 21.887 10.368 0.045 Mauritania 2005 10.010 37.268 30.724 3154087 22.252 52.264 12.126 0.045 Mauritania 2006 7.509 5.084 47.808 3241762 23.145 32.578 6.241 0.050 Mauritania 2007 6.098 4.152 43.176 3328285 35.143 7.254 0.048 Mauritania 2008 3.804 9.560 58.963 3414552 18.825 39.566 7.347 0.048 Mauritania 2009 4.086 -0.101 45.004 3501927 20.131 43.714 2.221 0.047 Mauritius 1990 24.369 1.547 64.957 1058775 52.409 44.687 13.488 0.265 Mauritius 1991 23.942 0.608 62.305 1070266 49.429 7.001 0.269 Mauritius 1992 23.829 0.456 59.305 1084441 58.212 52.557 4.644 0.272 Mauritius 1993 23.312 0.451 58.243 1097374 57.556 10.518 0.272 Mauritius 1994 22.628 0.562 56.723 1112846 63.989 7.323 0.266 Mauritius 1995 22.950 0.463 58.658 1122457 66.147 6.029 0.265 Mauritius 1996 23.383 0.829 63.586 1133996 66.757 62.282 6.551 0.266 Mauritius 1997 23.576 1.320 61.462 1148284 68.371 70.764 6.833 0.263 Mauritius 1998 23.957 0.292 65.684 1160421 72.114 75.631 6.811 0.264 Mauritius 1999 23.882 1.150 63.935 1175267 74.981 75.241 6.909 0.254 Mauritius 2000 23.479 5.797 61.386 1186873 76.817 70.783 4.199 0.261 Mauritius 2001 23.331 -0.610 68.457 1196287 78.666 74.422 5.389 0.259 Mauritius 2002 22.442 0.673 61.819 1204621 81.026 74.112 6.461 0.255 Mauritius 2003 21.679 1.116 56.678 1213370 84.593 98.316 3.924 0.252 Mauritius 2004 20.956 0.218 54.021 1221003 85.910 102.240 4.710 0.251 Mauritius 2005 19.848 0.665 59.855 1228254 88.656 107.200 4.942 0.239 Mauritius 2006 19.975 1.641 61.615 1233996 87.555 102.138 8.933 0.235 Mauritius 2007 19.220 4.373 57.864 1239630 88.442 99.259 8.803 0.238 Mauritius 2008 19.385 3.918 52.933 1244121 88.130 107.940 9.733 0.247 Mauritius 2009 18.809 2.905 48.961 1247429 88.866 99.702 2.550 0.262 Mozambique 1990 10.167 0.366 8.174 13371971 7.046 15.592 47.005 0.017 Mozambique 1991 10.969 0.818 10.156 13719853 7.017 10.716 32.933 0.017 Mozambique 1992 11.365 1.285 13.055 14203987 6.719 8.285 45.485 0.015 Mozambique 1993 8.461 1.578 12.934 14775877 6.739 3.195 42.200 0.015 Mozambique 1994 8.946 1.618 14.073 15363065 6.807 5.058 63.183 0.015 Mozambique 1995 7.615 2.003 15.601 15913101 7.274 4.801 54.434 0.014 Mozambique 1996 8.842 2.236 14.786 16410777 1.739 48.491 0.014 Mozambique 1997 9.784 1.690 13.354 16872896 2.837 7.369 0.015 Mozambique 1998 11.127 4.919 12.249 17317376 2.756 1.480 0.015 Mozambique 1999 11.772 8.414 13.166 17774066 5.132 5.464 2.860 0.015 Mozambique 2000 12.241 3.230 16.480 18264536 6.035 9.261 12.724 0.014 Mozambique 2001 13.891 6.268 23.385 18792357 6.819 7.832 9.050 0.015 89 University of Ghana http://ugspace.ug.edu.gh Mozambique 2002 13.887 8.273 27.339 19348715 8.445 10.207 16.781 0.016 Mozambique 2003 16.579 7.216 28.885 19928496 8.541 13.426 0.016 Mozambique 2004 17.197 4.295 30.878 20523159 10.766 6.790 12.663 0.016 Mozambique 2005 15.128 1.861 31.728 21126676 13.096 7.071 7.168 0.016 Mozambique 2006 15.566 2.612 38.358 21737860 15.250 6.895 13.239 0.016 Mozambique 2007 14.986 5.186 35.350 22359637 17.780 8.633 8.163 0.016 Mozambique 2008 14.894 5.653 32.267 22994867 19.808 12.000 10.328 0.016 Mozambique 2009 13.984 9.259 24.789 23647815 22.324 20.019 3.252 0.017 Namibia 1990 13.780 1.258 51.916 1415447 37.974 16.346 0.173 Namibia 1991 12.952 4.833 53.124 1466152 43.230 16.160 0.178 Namibia 1992 13.735 4.167 52.193 1513689 48.447 36.361 0.177 Namibia 1993 14.143 1.942 51.903 1559480 51.883 42.689 0.162 Namibia 1994 13.169 3.013 48.489 1605828 55.888 43.415 0.161 Namibia 1995 12.986 4.368 49.488 1654214 55.536 49.296 0.157 Namibia 1996 9.966 3.686 50.583 1705349 55.053 49.198 0.151 Namibia 1997 11.070 2.503 47.526 1758097 56.092 44.331 0.145 Namibia 1998 12.184 2.831 45.968 1809920 55.537 43.516 0.139 Namibia 1999 11.376 0.047 46.161 1857320 57.373 43.245 0.133 Namibia 2000 12.828 3.041 40.877 1897953 60.170 42.746 0.128 Namibia 2001 12.607 1.019 41.172 1931005 64.397 43.460 0.124 Namibia 2002 13.053 1.524 46.003 1957749 63.910 42.147 0.124 Namibia 2003 14.797 0.674 43.387 1980531 63.694 46.501 7.136 0.123 Namibia 2004 13.628 1.335 39.811 2002745 63.580 49.604 4.137 0.128 Namibia 2005 13.561 5.409 40.451 2027026 63.529 55.591 2.282 0.122 Namibia 2006 15.617 7.643 39.852 2053915 63.686 52.561 4.961 0.123 Namibia 2007 17.023 7.601 50.734 2083174 64.843 48.864 6.548 0.122 Namibia 2008 14.023 8.491 53.159 2115703 45.526 9.095 0.123 Namibia 2009 14.742 6.206 47.304 2152357 45.874 9.452 0.123 Niger 1990 6.589 1.645 15.013 7911884 6.244 16.174 -0.776 0.030 Niger 1991 6.212 0.651 14.052 8168834 6.739 14.927 -7.797 0.030 Niger 1992 6.466 2.405 16.755 8442330 6.457 15.107 -4.476 0.026 Niger 1993 6.516 -2.138 15.642 8732500 6.418 12.382 -1.215 0.024 Niger 1994 6.314 -0.720 16.532 9039088 11.242 36.041 0.023 Niger 1995 6.402 0.382 17.155 9361912 8.654 10.563 0.022 Niger 1996 6.471 0.497 16.946 9701730 6.524 8.784 5.289 0.021 Niger 1997 6.591 0.985 16.392 10058960 6.768 10.217 2.933 0.020 Niger 1998 6.187 -0.049 17.769 10432657 6.758 9.230 4.548 0.021 Niger 1999 6.494 0.014 15.894 10821434 7.039 9.387 -2.302 0.019 Niger 2000 6.795 0.469 17.799 11224523 6.948 9.187 2.900 0.017 Niger 2001 6.564 1.177 16.921 11642308 6.833 7.994 4.006 0.017 Niger 2002 6.590 0.111 15.204 12075991 6.862 8.518 2.629 0.017 90 University of Ghana http://ugspace.ug.edu.gh Niger 2003 6.350 0.546 16.031 12526725 7.460 9.217 -1.614 0.016 Niger 2004 6.525 0.862 16.078 12996012 9.011 11.555 0.263 0.015 Niger 2005 6.059 1.461 15.038 13485436 9.908 10.683 7.797 0.014 Niger 2006 5.382 1.105 13995530 11.308 7.958 0.040 0.014 Niger 2007 5.182 2.306 14527631 10.898 6.909 0.054 0.013 Niger 2008 4.859 5.250 15085130 11.276 6.107 11.305 0.014 Niger 2009 5.033 12.014 15672194 11.847 12.214 0.583 0.014 Nigeria 1990 5.495 2.065 43.431 95617345 24.596 21.902 7.364 0.062 Nigeria 1991 6.201 2.608 37.217 98085436 21.457 13.007 0.062 Nigeria 1992 5.070 2.741 42.239 1.01E+08 30.799 44.589 0.060 Nigeria 1993 5.701 6.301 47.121 1.03E+08 39.240 57.165 0.057 Nigeria 1994 6.990 8.280 41.756 1.06E+08 46.440 57.032 0.053 Nigeria 1995 5.446 3.840 44.288 1.08E+08 23.617 72.836 0.052 Nigeria 1996 4.917 4.514 48.145 1.11E+08 13.257 29.268 0.050 Nigeria 1997 5.143 4.249 44.952 1.14E+08 12.587 8.530 0.048 Nigeria 1998 5.224 3.271 33.525 1.17E+08 18.197 9.996 0.046 Nigeria 1999 4.726 2.890 36.898 1.20E+08 23.416 19.081 6.618 0.043 Nigeria 2000 3.667 2.479 53.977 1.23E+08 24.460 10.006 6.933 0.042 Nigeria 2001 4.213 2.481 42.994 1.26E+08 26.861 19.301 18.874 0.041 Nigeria 2002 3.426 3.170 31.867 1.29E+08 29.421 19.549 12.877 0.040 Nigeria 2003 3.390 2.964 42.703 1.33E+08 21.197 14.032 0.041 Nigeria 2004 3.061 2.133 43.951 1.36E+08 34.752 11.701 14.998 0.042 Nigeria 2005 2.832 4.439 46.537 1.40E+08 34.699 8.600 17.863 0.041 Nigeria 2006 2.578 3.338 42.868 1.43E+08 34.187 4.909 8.240 0.041 Nigeria 2007 2.522 3.637 41.020 1.47E+08 31.614 19.200 5.382 0.041 Nigeria 2008 2.410 3.957 42.755 1.51E+08 35.098 26.554 11.578 0.042 Nigeria 2009 2.470 5.074 34.987 1.55E+08 38.905 37.105 11.538 0.045 Rwanda 1990 18.305 0.297 5.615 7259740 16.257 17.074 4.186 0.031 Rwanda 1991 16.140 0.239 7.317 7071393 16.252 12.993 19.637 0.031 Rwanda 1992 12.106 0.108 5.568 6712924 15.700 15.215 9.560 0.033 Rwanda 1993 11.480 0.297 5.175 6300358 17.908 12.354 0.032 Rwanda 1994 17.250 0.000 6.303 5995987 28.453 0.016 Rwanda 1995 10.220 0.171 5.151 5912755 12.979 0.021 Rwanda 1996 11.545 0.160 6.031 6097688 10.211 7.411 0.022 Rwanda 1997 12.037 0.140 7.797 6506118 12.470 12.015 0.022 Rwanda 1998 11.235 0.356 5.585 7047196 12.707 6.210 0.021 Rwanda 1999 7.535 0.095 6.222 7585143 9.333 14.742 -2.406 0.019 Rwanda 2000 6.969 0.480 6.320 8021875 10.657 13.055 3.900 0.019 Rwanda 2001 6.908 0.277 8.479 8329113 10.847 12.786 3.343 0.019 Rwanda 2002 7.487 0.156 7.035 8539029 11.563 11.514 1.993 0.020 Rwanda 2003 6.791 0.252 8.453 8686469 12.853 12.354 7.450 0.020 91 University of Ghana http://ugspace.ug.edu.gh Rwanda 2004 6.938 0.367 11.124 8828956 14.513 9.432 12.251 0.020 Rwanda 2005 7.028 0.311 11.424 9008230 15.987 8.043 9.014 0.020 Rwanda 2006 6.799 0.361 11.058 9231041 18.060 8.883 0.020 Rwanda 2007 6.100 1.796 11.165 9481083 20.662 9.081 0.020 Rwanda 2008 6.157 2.193 14.457 9750314 22.670 15.445 0.021 Rwanda 2009 6.380 2.259 10.136 10024594 27.208 10.394 0.023 Senegal 1990 15.257 0.995 25.411 7514201 15.171 33.645 0.325 0.064 Senegal 1991 15.433 -0.134 23.093 7749559 32.224 -1.754 0.063 Senegal 1992 16.381 0.356 22.349 7990736 15.487 31.787 -0.110 0.059 Senegal 1993 15.732 -0.014 20.264 8234147 30.642 -0.586 0.056 Senegal 1994 16.030 1.725 31.754 8475136 24.574 32.294 0.052 Senegal 1995 16.594 0.649 30.866 8710746 22.098 7.864 0.051 Senegal 1996 16.979 0.172 26.984 8939438 14.724 22.207 2.754 0.049 Senegal 1997 16.464 3.767 27.315 9163184 14.539 21.432 1.753 0.047 Senegal 1998 16.005 1.404 27.697 9386923 14.476 20.942 1.157 0.046 Senegal 1999 15.885 2.979 28.056 9617641 15.287 21.283 0.827 0.046 Senegal 2000 14.651 1.345 27.925 9860578 15.847 23.492 0.732 0.044 Senegal 2001 16.899 0.655 28.726 10118078 16.079 23.421 3.074 0.043 Senegal 2002 17.239 1.463 28.549 10389457 17.397 21.310 2.231 0.041 Senegal 2003 16.324 0.765 26.627 10673320 21.292 -0.030 0.041 Senegal 2004 16.316 0.959 26.441 10967016 20.002 20.764 0.508 0.040 Senegal 2005 15.161 1.930 26.900 11268994 22.017 22.456 1.705 0.039 Senegal 2006 14.193 3.091 25.597 11578430 23.711 22.936 2.113 0.038 Senegal 2007 14.313 3.101 25.367 11897230 24.470 5.853 0.037 Senegal 2008 12.946 3.391 26.128 12229703 29.744 24.485 5.770 0.037 Senegal 2009 12.985 2.586 24.410 12581624 26.558 -2.248 0.038 Seychelles 1990 10.096 5.491 15.327 70000 42.306 3.889 0.605 Seychelles 1991 10.823 5.231 13.044 70755 111.029 46.371 1.987 0.604 Seychelles 1992 11.899 2.079 11.060 71657 113.162 46.592 3.247 0.614 Seychelles 1993 10.672 4.014 10.900 72711 112.830 53.058 1.384 0.619 Seychelles 1994 11.420 6.368 10.477 73925 115.971 63.662 1.737 0.576 Seychelles 1995 12.660 9.028 10.475 75304 112.993 72.138 -0.244 0.542 Seychelles 1996 13.017 5.769 27.937 76417 114.367 78.904 -1.100 0.535 Seychelles 1997 14.377 9.485 20.117 77319 79.650 0.618 0.563 Seychelles 1998 15.060 8.746 20.106 78846 99.566 92.329 2.580 0.572 Seychelles 1999 15.125 8.863 23.289 80410 73.726 103.363 6.347 0.543 Seychelles 2000 19.210 3.956 31.551 81131 72.868 110.057 6.269 0.533 Seychelles 2001 18.076 10.468 34.871 81202 71.224 117.314 5.970 0.509 Seychelles 2002 18.246 6.836 32.650 83700 70.657 129.164 0.175 0.487 Seychelles 2003 16.412 8.310 39.074 82800 74.968 135.710 3.303 0.445 Seychelles 2004 7.658 4.529 34.658 82500 74.972 110.492 3.857 0.413 92 University of Ghana http://ugspace.ug.edu.gh Seychelles 2005 8.743 9.344 36.965 82900 81.795 102.693 0.907 0.424 Seychelles 2006 8.963 14.346 37.441 84600 85.532 -0.353 0.434 Seychelles 2007 9.573 10.763 35.190 85033 78.487 76.257 5.321 0.457 Seychelles 2008 9.294 12.343 44.491 86956 75.653 66.004 36.965 0.435 Seychelles 2009 7.824 13.555 46.296 87298 75.524 43.055 31.754 0.446 Sierra Leone 1990 4.610 4.993 34.690 3931208 17.541 36.267 110.946 0.043 Sierra Leone 1991 5.323 0.962 31.249 3945899 17.207 19.179 102.695 0.044 Sierra Leone 1992 7.206 -0.823 31.591 3929182 11.960 65.500 0.034 Sierra Leone 1993 10.141 -0.971 24.283 3893891 9.388 22.209 0.033 Sierra Leone 1994 9.896 -0.315 29.512 3858559 90.045 24.204 0.032 Sierra Leone 1995 9.270 0.837 18.588 3837807 64.809 25.981 0.028 Sierra Leone 1996 8.488 0.070 17.445 3833053 52.221 23.137 0.027 Sierra Leone 1997 4.895 0.212 13.200 3843472 60.139 14.950 0.024 Sierra Leone 1998 3.985 0.016 14.062 3878475 51.133 35.533 0.023 Sierra Leone 1999 3.072 0.080 13.828 3948800 50.080 34.084 0.021 Sierra Leone 2000 3.549 6.133 18.135 4060709 54.409 -0.836 0.021 Sierra Leone 2001 3.830 0.911 7.920 4220198 27.136 35.953 2.090 0.018 Sierra Leone 2002 3.463 0.840 8.648 4422154 30.343 -3.286 0.022 Sierra Leone 2003 2.953 0.628 14.067 4647701 27.264 7.600 0.022 Sierra Leone 2004 2.776 4.273 16.670 4870467 22.564 14.188 0.021 Sierra Leone 2005 2.643 5.574 17.817 5071271 18.512 12.051 0.020 Sierra Leone 2006 2.795 3.161 17.069 5243214 17.427 9.539 0.019 Sierra Leone 2007 2.632 4.488 15.834 5391108 8.610 11.656 0.019 Sierra Leone 2008 2.599 2.150 13.717 5521838 11.991 -35.837 0.020 Sierra Leone 2009 2.276 4.599 13.698 5647194 14.177 9.252 0.021 South Africa 1990 23.640 -0.068 24.237 35200000 66.070 97.801 14.321 0.346 South Africa 1991 22.873 0.211 21.750 35933108 69.508 15.335 0.330 South Africa 1992 21.860 0.003 21.343 36690739 119.790 13.875 0.304 South Africa 1993 21.145 0.009 22.478 37473796 125.281 9.717 0.290 South Africa 1994 20.918 0.276 22.103 38283223 79.614 131.666 8.939 0.279 South Africa 1995 21.221 0.826 22.772 39120000 135.654 8.680 0.272 South Africa 1996 20.182 0.568 24.729 40000247 135.939 7.354 0.266 South Africa 1997 19.868 2.561 24.596 40926063 134.537 8.598 0.253 South Africa 1998 19.396 0.410 25.653 41899683 92.021 135.606 6.881 0.238 South Africa 1999 18.546 1.129 25.335 42923485 90.767 149.448 5.181 0.226 South Africa 2000 18.982 0.729 27.871 44000000 87.294 148.565 5.339 0.218 South Africa 2001 19.059 6.136 30.128 44909738 87.266 179.476 5.702 0.214 South Africa 2002 19.163 1.332 32.923 45448096 87.151 155.249 9.164 0.214 South Africa 2003 19.384 0.466 27.880 46034026 87.081 159.923 5.859 0.209 South Africa 2004 19.201 0.320 26.422 46641103 88.501 168.160 1.385 0.205 South Africa 2005 18.493 2.640 27.380 47270063 88.913 178.156 3.399 0.202 93 University of Ghana http://ugspace.ug.edu.gh South Africa 2006 17.458 -0.070 30.006 47921682 90.977 192.503 4.642 0.201 South Africa 2007 16.950 2.005 31.292 48596781 91.723 192.660 7.098 0.202 South Africa 2008 16.635 3.522 35.785 49296223 89.496 167.941 11.536 0.205 South Africa 2009 50020918 90.172 181.450 7.130 Swaziland 1990 36.818 2.701 59.021 862728 10.431 13.092 0.169 Swaziland 1991 36.721 7.104 59.259 887248 10.046 8.934 0.164 Swaziland 1992 35.017 6.796 57.349 907947 49.293 14.974 7.558 0.159 Swaziland 1993 36.140 5.299 57.286 926224 46.675 14.654 12.023 0.155 Swaziland 1994 36.050 4.457 63.679 944223 47.099 16.869 13.769 0.148 Swaziland 1995 39.061 3.046 60.016 963428 47.194 11.194 12.289 0.147 Swaziland 1996 37.832 1.355 59.355 984506 47.337 12.152 6.425 0.143 Swaziland 1997 39.465 -0.892 61.351 1006760 12.387 7.125 0.137 Swaziland 1998 39.341 9.685 67.174 1028694 45.785 10.453 8.110 0.134 Swaziland 1999 38.733 6.349 65.000 1048151 44.486 11.140 6.089 0.131 Swaziland 2000 38.674 5.947 74.327 1063715 41.920 11.120 12.209 0.127 Swaziland 2001 39.680 2.174 85.441 1074765 42.029 10.891 5.942 0.125 Swaziland 2002 39.191 7.519 95.711 1082195 42.112 14.944 12.020 0.124 Swaziland 2003 39.877 -3.285 100.949 1087949 41.414 16.644 7.290 0.122 Swaziland 2004 39.563 2.875 84.929 1094775 44.757 17.976 3.445 0.119 Swaziland 2005 38.928 -1.774 87.067 1104642 46.620 17.237 4.774 0.115 Swaziland 2006 41.354 4.106 76.616 1118204 50.339 13.429 5.305 0.113 Swaziland 2007 41.724 1.228 75.669 1134853 53.987 6.460 8.076 0.113 Swaziland 2008 42.234 3.501 59.361 1153750 1.819 12.657 0.113 Swaziland 2009 42.272 2.078 58.839 1173529 53.994 8.531 7.448 0.117 Tanzania 1990 9.273 0.000 12.621 25458208 34.592 35.827 0.036 Tanzania 1991 8.965 0.000 10.262 26307482 5.345 29.992 28.696 0.035 Tanzania 1992 8.197 0.264 12.442 27203865 5.354 29.316 21.847 0.033 Tanzania 1993 7.494 0.480 17.983 28122799 5.344 32.505 25.277 0.031 Tanzania 1994 7.408 1.108 20.614 29030288 5.312 27.104 34.083 0.029 Tanzania 1995 7.170 2.282 24.075 29903329 5.320 22.972 27.428 0.028 Tanzania 1996 7.366 2.310 19.937 30733937 5.217 15.702 20.977 0.027 Tanzania 1997 6.896 2.055 16.218 31533781 5.636 12.423 16.091 0.026 Tanzania 1998 10.552 1.844 12.398 32323953 10.970 12.800 0.025 Tanzania 1999 9.684 5.328 12.530 33135281 11.566 7.890 0.025 Tanzania 2000 9.387 4.549 13.365 33991590 10.711 5.924 0.024 Tanzania 2001 8.982 3.744 17.007 34899062 8.461 5.147 0.024 Tanzania 2002 8.894 3.667 17.581 35855480 8.859 5.318 0.025 Tanzania 2003 8.894 3.124 18.563 36866228 7.301 5.304 0.025 Tanzania 2004 8.707 1.768 19.651 37935334 7.448 4.736 0.025 Tanzania 2005 8.694 6.615 20.823 39065600 9.660 5.035 0.024 Tanzania 2006 8.571 2.812 22.562 40260847 8.702 7.251 0.024 94 University of Ghana http://ugspace.ug.edu.gh Tanzania 2007 8.560 3.456 24.242 41522004 10.576 7.026 0.024 Tanzania 2008 8.623 6.678 25.142 42844744 12.883 10.278 0.025 Tanzania 2009 9.544 4.458 23.228 44222113 13.572 12.142 0.027 Togo 1990 9.928 1.120 33.474 3786942 21.527 21.328 1.015 0.042 Togo 1991 11.116 0.404 33.443 3886858 20.428 22.956 0.387 0.040 Togo 1992 11.592 -0.774 26.935 3984356 20.170 21.426 1.394 0.036 Togo 1993 9.100 -0.963 24.384 4081398 27.941 -1.007 0.029 Togo 1994 9.121 1.569 30.549 4180689 19.931 21.653 39.163 0.031 Togo 1995 9.894 1.999 32.437 4284286 21.989 25.975 16.434 0.032 Togo 1996 9.234 1.181 33.282 4392941 23.522 24.959 4.688 0.032 Togo 1997 8.416 1.401 28.975 4506465 24.055 22.907 8.251 0.034 Togo 1998 6.941 1.900 29.686 4624826 23.684 0.973 0.030 Togo 1999 6.410 2.700 28.875 4747665 29.610 21.253 -0.071 0.029 Togo 2000 8.581 3.239 34.384 4874735 32.124 22.416 1.890 0.026 Togo 2001 8.847 4.772 33.801 5006223 34.865 19.361 3.910 0.024 Togo 2002 9.084 3.619 36.517 5142419 38.941 15.964 3.074 0.023 Togo 2003 9.089 2.016 43.368 5283246 41.105 18.901 -0.963 0.023 Togo 2004 8.199 3.064 38.565 5428552 42.606 17.743 0.392 0.022 Togo 2005 8.543 4.537 40.044 5578219 45.030 17.794 6.802 0.020 Togo 2006 9.259 4.145 38.201 5732175 47.007 17.283 2.227 0.020 Togo 2007 9.185 2.470 37.919 5890414 44.025 21.763 0.960 0.019 Togo 2008 8.477 1.602 35.494 6052937 22.722 8.682 0.019 Togo 2009 7.907 1.458 36.741 6219761 27.268 3.313 0.020 Uganda 1990 5.671 -0.137 7.241 17384369 11.420 33.119 0.024 Uganda 1991 5.818 0.030 7.464 17973428 10.555 28.068 0.024 Uganda 1992 6.173 0.105 8.761 18571527 10.206 17.751 52.442 0.024 Uganda 1993 5.976 1.695 7.063 19177660 10.046 12.104 1.164 0.024 Uganda 1994 6.521 2.210 8.741 19791266 10.209 9.232 10.037 0.023 Uganda 1995 6.792 2.106 11.792 20412967 10.450 4.378 6.550 0.024 Uganda 1996 7.862 2.002 11.961 21041468 5.151 7.192 0.025 Uganda 1997 8.577 2.791 13.360 21679497 9.856 6.565 8.169 0.024 Uganda 1998 9.108 3.189 9.639 22336812 7.526 0.069 0.023 Uganda 1999 9.832 2.337 12.252 23026357 8.044 5.777 0.023 Uganda 2000 7.583 2.595 10.651 23757636 16.546 12.203 3.392 0.022 Uganda 2001 7.529 2.594 11.518 24534668 16.712 8.861 1.865 0.022 Uganda 2002 7.816 2.989 11.213 25355794 19.487 13.205 -0.288 0.023 Uganda 2003 7.528 3.191 11.387 26217760 19.632 10.088 8.680 0.022 Uganda 2004 6.731 3.720 12.697 27114742 19.374 8.220 3.721 0.022 Uganda 2005 7.465 4.112 14.180 28042413 19.553 8.636 8.449 0.021 Uganda 2006 7.550 6.457 15.275 29000925 21.115 7.511 7.311 0.022 Uganda 2007 7.558 6.649 16.725 29991958 24.516 5.492 6.139 0.022 95 University of Ghana http://ugspace.ug.edu.gh Uganda 2008 7.769 5.047 24.279 31014427 26.099 12.122 12.051 0.023 Uganda 2009 8.031 5.325 23.746 32067125 27.367 10.293 13.017 0.025 Zambia 1990 36.061 6.164 35.880 8143142 20.434 67.803 107.024 0.054 Zambia 1991 36.748 1.016 34.613 8361381 83.292 97.642 0.052 Zambia 1992 37.163 1.414 36.391 8576987 165.707 0.048 Zambia 1993 27.945 9.604 33.570 8794061 35.760 183.312 0.048 Zambia 1994 11.243 1.195 36.005 9018229 19.686 48.195 54.601 0.041 Zambia 1995 11.221 2.789 36.017 9253527 58.399 34.930 0.037 Zambia 1996 13.368 3.581 31.326 9502346 53.500 43.073 0.037 Zambia 1997 13.195 5.304 30.121 9763742 40.959 24.419 0.035 Zambia 1998 12.973 6.116 26.903 10034412 60.164 24.458 0.032 Zambia 1999 12.085 5.174 27.220 10309310 55.247 26.788 0.031 Zambia 2000 11.342 3.741 26.476 10585220 63.962 26.030 0.029 Zambia 2001 11.020 3.968 28.138 10861238 44.840 21.394 0.029 Zambia 2002 11.526 8.040 28.504 11139978 41.398 22.233 0.029 Zambia 2003 12.015 7.992 28.336 11426006 33.890 21.402 0.029 Zambia 2004 11.894 7.153 37.778 11725635 30.675 17.968 0.028 Zambia 2005 11.648 4.972 34.566 12043591 19.435 18.324 0.027 Zambia 2006 11.141 5.754 38.491 12381509 13.974 9.020 0.027 Zambia 2007 10.196 11.471 40.913 12738676 13.458 10.657 0.027 Zambia 2008 9.958 6.411 35.375 13114579 15.409 12.446 0.027 Zambia 2009 9.662 5.426 35.013 13507849 15.483 13.395 0.029 Zimbabwe 1990 22.756 -0.139 22.867 10484771 45.080 41.724 17.363 0.053 Zimbabwe 1991 27.156 0.032 23.883 10763036 46.620 39.293 23.342 0.054 Zimbabwe 1992 29.537 0.221 27.227 11019717 41.524 43.121 42.065 0.046 Zimbabwe 1993 23.012 0.426 30.720 11256512 38.963 47.869 27.588 0.044 Zimbabwe 1994 21.167 0.503 34.600 11476807 38.907 43.814 22.264 0.045 Zimbabwe 1995 21.795 1.655 38.236 11683136 40.507 52.283 22.594 0.042 Zimbabwe 1996 18.781 0.946 36.130 11877664 41.433 48.987 21.434 0.044 Zimbabwe 1997 18.007 1.584 37.595 12059858 43.162 63.058 18.736 0.042 Zimbabwe 1998 16.628 6.940 43.393 12226742 58.186 31.820 0.041 Zimbabwe 1999 16.353 0.860 37.409 12374019 42.722 37.331 58.520 0.038 Zimbabwe 2000 15.605 0.347 38.160 12499981 42.562 52.240 55.866 0.035 Zimbabwe 2001 14.559 0.056 34.959 12603988 43.339 70.837 76.707 0.034 Zimbabwe 2002 13.251 0.408 31.835 12691431 41.252 164.559 140.060 0.030 Zimbabwe 2003 13.647 0.066 32.397 12774162 37.619 80.196 431.700 0.024 Zimbabwe 2004 15.117 0.150 34.470 12867828 40.301 282.380 0.021 Zimbabwe 2005 16.383 1.786 33.549 12984418 55.330 302.117 0.019 Zimbabwe 2006 16.889 0.735 35.956 13127942 1096.678 0.017 Zimbabwe 2007 16.401 1.302 37.785 13297798 24411.031 0.016 Zimbabwe 2008 16.659 1.169 41.467 13495462 0.013 96 University of Ghana http://ugspace.ug.edu.gh Zimbabwe 2009 17.754 1.712 29.278 13720997 0.014 97